The growth and sustainability of today’s global economy heavily relies on smooth maritime operations. The increasing security concerns to marine environments pose complex security challenges, such as smuggling, illegal fishing, human trafficking, and environmental threats, for traditional surveillance methods due to their limitations. Artificial intelligence (AI), particularly deep learning, has offered strong capabilities for automating object detection, anomaly identification, and situational awareness in maritime environments. In this paper, we have reviewed the state-of-the-art deep learning models mainly proposed in recent literature (2020–2025), including convolutional neural networks, recurrent neural networks, Transformers, and multimodal fusion architectures. We have highlighted their success in processing diverse data sources such as satellite imagery, AIS, SAR, radar, and sensor inputs from UxVs. Additionally, multimodal data fusion techniques enhance robustness by integrating complementary data, yielding more detection accuracy. There still exist challenges in detecting small or occluded objects, handling cluttered scenes, and interpreting unusual vessel behaviours, especially under adverse sea conditions. Additionally, explainability and real-time deployment of AI models in operational settings are open research areas. Overall, the review of existing maritime literature suggests that deep learning is rapidly transforming maritime domain awareness and response, with significant potential to improve global maritime security and operational efficiency. We have also provided key datasets for deep learning models in the maritime security domain.

This study reconsiders the Fisher effect for the UK from a different methodological perspective. To this aim, the nonlinear ARDL model recently developed by Shin et al. (2014), is applied over the periods of 1995M1-2008M9 and 2008M10-2018M1. This model decomposes the changes in original inflation series as two new series: increases and decreases in inflation rates. Hence, it enables us to examine the Fisher effect in terms of increases and decreases in inflation separately. The empirical findings support asymmetrically partial Fisher effects for the UK in the long-run only for the first period. Additionally, this study attempts to describe and introduce a different version of the partial effect concept for the first time for the UK.

The growth and sustainability of today's global economy heavily relies on smooth maritime operations. The increasing security concerns to marine environments pose complex security challenges, such as smuggling, illegal fishing, human trafficking, and environmental threats, for traditional surveillance methods due to their limitations. Artificial intelligence (AI), particularly deep learning, has offered strong capabilities for automating object detection, anomaly identification, and situational awareness in maritime environments. In this paper, we have reviewed the state-of-the-art deep learning models, including convolutional neural networks, recurrent neural networks, Transformers, and multimodal fusion architectures. We have highlighted their success in processing diverse data sources such as satellite imagery, AIS, SAR, radar, and sensor inputs from UxVs. Additionally, multimodal data fusion techniques enhance robustness by integrating complementary data, yielding more detection accuracy. There still exist challenges in detecting small or occluded objects, handling cluttered scenes, and interpreting unusual vessel behaviours, especially under adverse sea conditions. Additionally, explainability and real-time deployment of AI models in operational settings are open research areas. Overall, the review of existing maritime literature suggests that deep learning is rapidly transforming maritime domain awareness and response, with significant potential to improve global maritime security and operational efficiency. We have also provided key datasets for deep learning models in maritime security domain.

This study aims to reveal the need to reformulate new forms of Bilateral Trade Balances (BTBs) for a country rather than a traditional BTB. This is because the traditional BTB ratio, based on total exports and defined as the total exports/total imports ratio, cannot classify and quantify a BTB based on its economic impact content. It fails to classify because countries also export goods already imported (denotes re-export) besides exporting their domestic goods produced within the country (denotes domestic export). It also fails to quantify because, while domestic goods undergo a value-added process within a country, re-exported goods do not. In this context, for the first time, this study attempts to reformulate/reinvestigate new forms of BTBs as production-related BTB, based on domestic export and non-production-related BTB, based on re-export for the USA with Japan. Empirical findings confirm the necessity and cruciality of the proposed methodology in this study. JEL Codes: F10, F14