Is the war entering a new phase? Bulgarian authorities have ended search operations after confirming no survivors were found following a drone strike that sank a ship off the country's coast. Drones hit two commercial vessels early on Tuesday as attacks on Black Sea shipping increase amid an intensifying Russia-Ukraine war . One caught fire, and the other sank. The search stopped late on Tuesday and would not continue, he added.

A Togo-flagged merchant ship sank off Bulgaria after a drone attack, with its crew missing. A second ship was also hit and its crew was rescued. Bulgaria's prime minister called the attacks a'flagrant violation' of international and maritime law. Yemen's Taiz residents fear food and fuel crisis amid fighting Is Europe preparing for a wider war with Russia? What we know about Russia's suspected plague case France's student protests grow as hundreds of schools close

Is the war entering a new phase? Bulgarian Prime Minister Rumen Radev says drones have hit two commercial vessels, sinking one ship and wounding several people, in what he denounced as an "absolutely unacceptable" attack. Speaking to reporters on Tuesday, Radev said the ships were attacked by sea drones and aerial drones at about 3am (00:00 GMT), about 70 nautical miles (130km) off Bulgaria's coast, amid a rise in attacks on Black Sea shipping due to expanding drone attacks related to the Russia-Ukraine war. It is a blatant violation of international law and maritime law," Radev said. The Togo-flagged Alfa Watan suffered critical structural damage and sank at the scene.

Bulgaria is investigating the cause of an overnight fire at an ammunition storage site run by a private defence company. Interior Minister Ivan Demerdzhiev said the authorities are not ruling out outside interference in the incident in central Bulgaria. No-one was injured in the blaze that triggered a series of explosions. EMCO company, whose weapons are used by Ukraine against invading Russian forces, said the fire at its site was sabotage and that human error was completely ruled out. It did not say who might be responsible.

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Online health communities (OHCs) are vital for fostering peer support and improving health outcomes. Support groups within these platforms can provide more personalized and cohesive peer support, yet traditional support group formation methods face challenges related to scalability, static categorization, and insufficient personalization. To overcome these limitations, we propose two novel machine learning models for automated support group formation: the Group specific Dirichlet Multinomial Regression (gDMR) and the Group specific Structured Topic Model (gSTM). These models integrate user generated textual content, demographic profiles, and interaction data represented through node embeddings derived from user networks to systematically automate personalized, semantically coherent support group formation. We evaluate the models on a large scale dataset from MedHelp, comprising over 2 million user posts. Both models substantially outperform baseline methods including LDA, DMR, and STM in predictive accuracy (held out log likelihood), semantic coherence (UMass metric), and internal group consistency. The gDMR model yields group covariates that facilitate practical implementation by leveraging relational patterns from network structures and demographic data. In contrast, gSTM emphasizes sparsity constraints to generate more distinct and thematically specific groups. Qualitative analysis further validates the alignment between model generated groups and manually coded themes, showing the practical relevance of the models in informing groups that address diverse health concerns such as chronic illness management, diagnostic uncertainty, and mental health. By reducing reliance on manual curation, these frameworks provide scalable solutions that enhance peer interactions within OHCs, with implications for patient engagement, community resilience, and health outcomes.

The main cause of high latency in LLMs is the sequential decoding process, which autoregressively generates all labels and mentions for NER, significantly increase the sequence length.