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.

Money laundering and financial fraud remain major threats to global financial stability, costing trillions annually and challenging regulatory oversight. This paper reviews how artificial intelligence (AI) applications can modernize Anti-Money Laundering (AML) workflows by improving detection accuracy, lowering false-positive rates, and reducing the operational burden of manual investigations, thereby supporting more sustainable development. It further highlights future research directions including federated learning for privacy-preserving collaboration, fairness-aware and interpretable AI, reinforcement learning for adaptive defenses, and human-in-the-loop visualization systems to ensure that next-generation AML architectures remain transparent, accountable, and robust. In the final part, the paper proposes an AI-driven KYC application that integrates graph-based retrieval-augmented generation (RAG Graph) with generative models to enhance efficiency, transparency, and decision support in KYC processes related to money-laundering detection. Experimental results show that the RAG-Graph architecture delivers high faithfulness and strong answer relevancy across diverse evaluation settings, thereby enhancing the efficiency and transparency of KYC CDD/EDD workflows and contributing to more sustainable, resource-optimized compliance practices.

Semantic Textual Similarity measures the degree of equivalence between the two texts and is important in many Natural Language Processing tasks. While extensive resources have been developed for high - resource languages, unfortunately, low - resource languages, for example, Kurdish, have been neglected. In this paper, the first STS dataset for K urdish has been introduced, which aims to alleviate this gap. This dataset contains 10,000 formal and informal sentence pairs annotated for similarity. To this end, aft er benchmarking several models, such as Sentence Bidirectional Encoder Representations from Transformers (Sentence - BERT) and multilingual Bidirectional Encoder Representations from Transformers (multilingual BERT), among others, which achieved promising results while also showcasing the difficulties presented by the distinctive nature of Kurdish. This work paves the way for future studies in Kurdish semantic research and Natural Language Processing in general for other low - resource languages.

Pulsar surveys generate millions of candidates per run, overwhelming manual inspection. This thesis builds a deep learning pipeline for radio pulsar candidate selection that fuses array-derived features with image diagnostics. From approximately 500 GB of Giant Metrewave Radio Telescope (GMRT) data, raw voltages are converted to filterbanks (SIGPROC), then de-dispersed and folded across trial dispersion measures (PRESTO) to produce approximately 32,000 candidates. Each candidate yields four diagnostics--summed profile, time vs. phase, subbands vs. phase, and DM curve--represented as arrays and images. A baseline stacked model (ANNs for arrays + CNNs for images with logistic-regression fusion) reaches 68% accuracy. We then refine the CNN architecture and training (regularization, learning-rate scheduling, max-norm constraints) and mitigate class imbalance via targeted augmentation, including a GAN-based generator for the minority class. The enhanced CNN attains 87% accuracy; the final GAN+CNN system achieves 94% accuracy with balanced precision and recall on a held-out test set, while remaining lightweight enough for near--real-time triage. The results show that combining array and image channels improves separability over image-only approaches, and that modest generative augmentation substantially boosts minority (pulsar) recall. The methods are survey-agnostic and extensible to forthcoming high-throughput facilities.

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.

Human cognitive behavior arises from the interaction of specialized brain networks dedicated to distinct functions, such as language, logic, and social reasoning. Concretely, we partition the layers of a pretrained language model into four expert modules aligned with well-studied cognitive networks in the human brain. 's behavior can be dynamically steered at inference time by routing tokens to particular experts (e.g., favoring social over logical reasoning), enabling fine-grained control over outputs. Taken together, cognitively grounded functional specialization yields models that are both more humanlike and more human-interpretable. Neuroscience research suggests that distinct brain regions support language, reasoning, social cognition, and other cognitive functions (Saxe & Kanwisher, 2003; Kanwisher, 2010; Fedorenko et al., 2024). In contrast, the internal organization of Large Language Models (LLMs) is largely unstructured. While certain units or subnetworks show selective activation (Zhang et al., 2022; 2023; Bayazit et al., 2023; AlKhamissi et al., 2025a; Wang et al., 2025), such specialization is implicit and difficult to interpret or control. Motivated by this discrepancy, we propose a model architecture that explicitly incorporates specialization. On the machine learning (ML) side, such designs hold great potential for improving interpretability and controllability; on the cognitive science side, they provide a framework toward formulating testable computational hypotheses about how the relative contributions of different brain networks support complex behavior. The final training stage of this curriculum uses this now inductively-biased architecture to perform large-scale supervised finetuning.

The complexity and difficulties of Kurdish speaker detection among its several dialects are investigated in this work. Because of its great phonetic and lexical differences, Kurdish with several dialects including Kurmanji, Sorani, and Hawrami offers special challenges for speaker recognition systems. The main difficulties in building a strong speaker identification system capable of precisely identifying speakers across several dialects are investigated in this work. To raise the accuracy and dependability of these systems, it also suggests solutions like sophisticated machine learning approaches, data augmentation tactics, and the building of thorough dialect-specific corpus. The results show that customized strategies for every dialect together with cross-dialect training greatly enhance recognition performance.