Iran war: What is happening on day 19 of US-Israel attacks? Iran has pledged "revenge" after Israeli strikes killed security chief Ali Larijani and commander of Basij paramilitary forces Gholamreza Soleimani, with Foreign Minister Abbas Araghchi saying Tehran's political system remains strong as the war entered its 19th day . Iran launched more attacks on Israel, causing extensive property damage, after an earlier strike killed two people in Ramat Gan. Political tensions are also rising in the United States, as senior counterterrorism official Joe Kent resigned, saying "we started this war due to pressure from Israel and its powerful American lobby". Meanwhile, President Donald Trump criticised NATO allies and partners for failing to provide stronger military support in efforts to end Iran's chokehold on the Strait of Hormuz.

Could Iran be using China's BeiDou system? Drone strike hits Al-Rasheed hotel in Baghdad's Green Zone near US embassy, no casualties reported A prominent hotel in central Baghdad's heavily fortified Green Zone was struck by a drone, amid reports that Iraqi air defences intercepted an attack over the United States Embassy. The strike on Monday evening hit the top floor of Al-Rasheed Hotel, causing damage but no casualties, according to two Iraqi security officials cited by The Associated Press (AP) news agency. Security sources told the Reuters news agency that two Katyusha rockets had been intercepted that evening near the US Embassy in the Green Zone, which houses diplomatic missions as well as international institutions and government offices. Earlier Monday, the Iran-backed Kataib Hezbollah announced that Abu Ali Al-Askari, a prominent security official with the paramilitary group, had been killed, without giving details on the circumstances.

Iraq's Prime Minister Mohammed Shia al-Sudani faces pressure to act against Iran-backed terrorist groups following increased attacks on U.S., European, and Kurdish assets in the country.

British forces based in Iraq shot down two Iranian drones overnight, Defence Secretary John Healey has said. But some drones in the attack hit the coalition base in the Iraqi city of Erbil, the capital of the Kurdistan region, and injured a number of US troops. There were no British casualties. Brigadier Guy Foden said the base and another in the Iraqi capital of Baghdad were struck a number of times on Wednesday night and British personnel are currently in Erbil helping to defend that base. Since the US-Israeli strikes on Iran, US bases in Iraq have been targeted in retaliation.

The Syrian army is moving into areas east of Aleppo city, after Kurdish forces started a withdrawal. Syrian troops have been spotted entering Deir Hafer, a town about 50km (30 miles) from Aleppo. On Friday, the Kurdish Syrian Democratic Forces (SDF) militia announced it would redeploy east of the Euphrates river. This follows talks with US officials, and a pledge from Syrian President Ahmed al-Sharaa to make Kurdish a national language. After deadly clashes last week, the US urged both sides to avoid a confrontation.

Scientific literature is increasingly siloed by complex language, static disciplinary structures, and potentially sparse keyword systems, making it cumbersome to capture the dynamic nature of modern science. This study addresses these challenges by introducing an adaptable large language model (LLM)-driven framework to quantify thematic trends and map the evolving landscape of scientific knowledge. The approach is demonstrated over a 20-year collection of more than 1,500 engineering articles published by the Proceedings of the National Academy of Sciences (PNAS), marked for their breadth and depth of research focus. A two-stage classification pipeline first establishes a primary thematic category for each article based on its abstract. The subsequent phase performs a full-text analysis to assign secondary classifications, revealing latent, cross-topic connections across the corpus. Traditional natural language processing (NLP) methods, such as Bag-of-Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF), confirm the resulting topical structure and also suggest that standalone word-frequency analyses may be insufficient for mapping fields with high diversity. Finally, a disjoint graph representation between the primary and secondary classifications reveals implicit connections between themes that may be less apparent when analyzing abstracts or keywords alone. The findings show that the approach independently recovers much of the journal's editorially embedded structure without prior knowledge of its existing dual-classification schema (e.g., biological studies also classified as engineering). This framework offers a powerful tool for detecting potential thematic trends and providing a high-level overview of scientific progress.

In this paper, we focused on using deep learning methods for detecting money laundering in financial transaction networks, in order to demonstrate that it can be used as a complement or instead of the more commonly used rule-based systems and conventional Anti-Money Laundering (AML) systems. The paper explores the pivotal role played by Anti-Money Laundering (AML) activities in the global financial industry. It underscores the drawbacks of conventional AML systems, which exhibit high rates of false positives and lack the sophistication to uncover intricate money laundering schemes. To tackle these challenges, the paper proposes an advanced AML system that capitalizes on link analysis using deep learning techniques. At the heart of this system lies the utilization of centrality algorithms like Degree Centrality, Closeness Centrality, Betweenness Centrality, and PageRank. These algorithms enhance the system's capability to identify suspicious activities by examining the influence and interconnections within networks of financial transactions. The significance of Anti-Money Laundering (AML) efforts within the global financial sector is discussed in this paper. It highlights the limitations of traditional AML systems. The results showed the practicality and superiority of the new implementation of the GCN model, which is a preferable method for connectively structured data, meaning that a transaction or account is analyzed in the context of its financial environment. In addition, the paper delves into the prospects of Anti-Money Laundering (AML) efforts, proposing the integration of emerging technologies such as deep learning and centrality algorithms. This integration holds promise for enhancing the effectiveness of AML systems by refining their capabilities.

This paper enhances the study of sentiment analysis for the Central Kurdish language by integrating the Bidirectional Encoder Representations from Transformers (BERT) into Natural Language Processing techniques. Kurdish is a low - resourced language, having a high level of linguistic diversity with minimal computational resources, making sentiment analysis somewhat challenging. Earlier, this was done using a traditional w ord embedding model, such as Word2Vec, but with the emergence of new language models, specifically BERT, there is hope for improvements. The better word embedding capabilities of BERT lend to this study, aiding in the capturing of the nuanced semantic pool and the contextual intricacies of the language under study, the Kurdish language, thus setting a new benchmark for sentiment analysis in low - resource languages. The steps include collecting and normalizing a large corpus of Kurdish texts, pretraining BERT with a special tokenizer for Kurdish, and developing different models for sentiment analysis including Bidirectional Long Short - Term Memory ( BiLSTM), Multi - L ayer Perceptron ( MLP), and finetuning the BERT classifier . The proposed approach consists of 3 cla sses: positive, negative, and neutral sentiment analysis using a sentiment embedding of BERT in four different configurations. The accuracy of the best - performing classifier, BiLSTM, is 74.09%. For the BERT with an MLP classifier model, the maximum accuracy achieved is 73.96%, while the fine - tuned BERT model tops the others with 75.37% accuracy. Additionally, the fine - tuned BERT model demonstrates a vast improvement when focused on t wo 2 - class sentiment analyses positive and negative with an accuracy of 86.

Alzheimers Disease (AD) is a progressive neurodegenerative disorder that poses significant challenges in its early diagnosis, often leading to delayed treatment and poorer outcomes for patients. Traditional diagnostic methods, typically reliant on single data modalities, fall short of capturing the multifaceted nature of the disease. In this paper, we propose a novel multimodal framework for the early detection of AD that integrates data from three primary sources: MRI imaging, cognitive assessments, and biomarkers. This framework employs Convolutional Neural Networks (CNN) for analyzing MRI images and Long Short-Term Memory (LSTM) networks for processing cognitive and biomarker data. The system enhances diagnostic accuracy and reliability by aggregating results from these distinct modalities using advanced techniques like weighted averaging, even in incomplete data. The multimodal approach not only improves the robustness of the detection process but also enables the identification of AD at its earliest stages, offering a significant advantage over conventional methods. The integration of biomarkers and cognitive tests is particularly crucial, as these can detect Alzheimer's long before the onset of clinical symptoms, thereby facilitating earlier intervention and potentially altering the course of the disease. This research demonstrates that the proposed framework has the potential to revolutionize the early detection of AD, paving the way for more timely and effective treatments

By upgrading an existing deep learning model with the knowledge provided by one of the oldest sets of grayscale satellite imagery, known as CORONA, we improved the AI model's attitude towards the automatic identification of archaeological sites in an envir onment which has been completely transformed in the last five decades, including the complete destruction of many of those same sites. The initial Bing - based convolutional network model was re - trained using CORONA satellite imagery for the district of Abu Ghraib, west of Baghdad, central Mesopotamian floodplain. The results were twofold and surprising. First, the detection precision obtained on the area of interest increased sensibly: in particular, the Intersection - over - Union (IoU) values, at the image segmentation level, surpassed 85%, while the general accuracy in detecting archeological sites reached 90%. Second, our re - trained model allowed the identification of four new sites of archaeological interest (confirmed through field verification), previously not identified by archaeologists with traditional techniques. This has confirmed the efficacy of using AI techniques and the CORONA imagery from the 1960s to discover archaeological sites currently no longer visible, a concrete breakthrough with significant consequences for the study of landscapes with vanishing archaeological evidence induced by anthropization.