ADA University (ADA) (Azerbaijani: ADA Universiteti) is a university established under the Ministry of Foreign Affairs of Azerbaijan in March 2006 by Hafiz Pashayev. By the decree of President, ADA was transformed into university in 2014. When founded as the Azerbaijan Diplomatic Academy the main objective was to train specialists for a diplomatic career in Azerbaijani foreign affairs, however its programs expanded and the name changed to ADA University. Its founding rector is Ambassador Hafiz Pashayev, who is a Deputy Foreign Minister of Azerbaijan and a former ambassador of Azerbaijan to the United States. Its present, dedicated campus opened in September 2012.
Plastic pollution poses a major environmental threat to coastal ecosystems, particularly in enclosed and semi-enclosed seas where limited water exchange promotes debris accumulation. This study presents a high-resolution spatial analysis of coastal plastic debris along the Khachmaz coastline in the western Caspian Sea. The analysis integrates unmanned aerial vehicle (UAV) imagery, YOLO-based deep learning detection, and spatial statistical methods. High-resolution UAV orthophotos enabled the automated detection of individual plastic debris items, which were converted into spatial point data for further analysis. Spatial patterns were assessed using areal density estimation, nearest neighbor analysis, kernel density estimation, and Ripley's L-function to examine clustering across multiple spatial scales. A total of 2389 plastic debris items were identified within 0.0439 km2, corresponding to an average density of 54,382 items per km2. The results show that plastic debris is unevenly distributed, forming distinct clusters with four primary accumulation hotspots. Significant clustering occurs at spatial scales up to 20 m, with the strongest aggregation observed at distances below 5 m. Spatial overlay analysis indicates a strong association between plastic debris, reed-dominated coastal vegetation, and proximity to the shoreline, suggesting the potential role of localized retention processes and shoreline dynamics in debris accumulation. The combined use of UAV-based deep learning and spatial statistical analysis provides an integrated application framework for monitoring coastal plastic debris and supports targeted, sustainability-oriented coastal management strategies in the Caspian Sea region.
Over three decades, Azerbaijan's higher education has transformed from a Soviet-style education to a modern one, yet research on the influence of student engagement and experience on higher-order thinking and identity development remains limited. This qualitative study investigates student engagement and experience and their influence on higher-order thinking and identity development in higher education through the lenses of Weidman's socialisation model, Mezirow's transformative learning theory, and Tinto's three-stage model of student departure. Through maximum-variation sampling, 15 participants were recruited to conduct semi-structured interviews, and the qualitative data were analysed using thematic analysis. This case study identifies key barriers to higher-order thinking and identity development, including inadequate course content and low-quality materials, teacher-centred approaches, and university extracurricular activities. The university environment impedes the effectiveness of students' intellectual and non-intellectual socialisation, which results in accepting information at face value without question. Another factor contributing to low student engagement, influencing higher-order thinking and identity development, is that faculty are less likely to be passionate about their course design and caring for building knowledge by using a variety of teaching strategies that may help students recognise the importance of information they receive within their context. Extracurricular activities fall short in broadening students' social circles, enabling them to discover new ideas, learn from their peers, and share mutual interests, which foster higher-order thinking and identity development. The findings disclose that faculty expertise and performance in some universities may create a positive learning environment. Conversely, inadequate extracurricular activities compel students to seek opportunities beyond higher education, which reveals the problem in institutional support for student development.
This research investigates ambiguity, uncertainty arising from doubts about predictive models, and its impact on market returns. Traditional asset-pricing frameworks treat uncertainty as quantifiable risk with known probabilities, whereas ambiguity represents a deeper challenge because investors cannot fully trust the models that generate forecasts. A composite ambiguity measure is constructed using principal component analysis of three established proxies: the CBOE Volatility Index (VIX), the Economic Policy Uncertainty (EPU) Index, and the RavenPack News Sentiment (SENT) Index. The resulting measure captures belief dispersion across financial, policy, and informational domains within a single interpretable factor. Weekly U.S. data from September 2005 to January 2025 are analyzed to assess whether ambiguity helps explain variation in aggregate market returns. The study findings indicate that higher ambiguity is consistently associated with lower market returns, reflecting investor ambiguity aversion and reduced confidence in predictive models. The revealed pattern suggests that ambiguity amplifies return fluctuations in a state-dependent manner, with effects strengthening during periods of financial stress, such as the global financial crisis, the COVID-19 shock, and the 2022 monetary-tightening cycle. Overall, ambiguity emerges as persistent and state-dependent component of the U.S. equity market, with important implications for theory, policy, and portfolio design. Robustness across selected controls, PCA designs, and sample periods shows that the composite ambiguity factor (CAF) models uncertainty rather than transitory sentiment.
While defenses for structured PII are mature, Large Language Models (LLMs) pose a new threat: Semantic Sensitive Information (SemSI), where models infer sensitive identity attributes, generate reputation-harmful content, or hallucinate potentially wrong information. The capacity of LLMs to self-regulate these complex, context-dependent sensitive information leaks without destroying utility remains an open scientific question. To address this, we introduce SemSIEdit, an inference-time framework where an agentic "Editor" iteratively critiques and rewrites sensitive spans to preserve narrative flow rather than simply refusing to answer. Our analysis reveals a Privacy-Utility Pareto Frontier, where this agentic rewriting reduces leakage by 34.6
Current safety mechanisms for Large Language Models (LLMs) rely heavily on static, fine-tuned classifiers that suffer from adaptation rigidity, the inability to enforce new governance rules without expensive retraining. To address this, we introduce CourtGuard, a retrieval-augmented multi-agent framework that reimagines safety evaluation as Evidentiary Debate. By orchestrating an adversarial debate grounded in external policy documents, CourtGuard achieves state-of-the-art performance across 7 safety benchmarks, outperforming dedicated policy-following baselines without fine-tuning. Beyond standard metrics, we highlight two critical capabilities: (1) Zero-Shot Adaptability, where our framework successfully generalized to an out-of-domain Wikipedia Vandalism task (achieving 90% accuracy) by swapping the reference policy; and (2) Automated Data Curation and Auditing, where we leveraged CourtGuard to curate and audit nine novel datasets of sophisticated adversarial attacks. Our results demonstrate that decoupling safety logic from model weights offers a robust, interpretable, and adaptable path for meeting current and future regulatory requirements in AI governance.
