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Russian counter-trade as a mechanism for promoting arms sales and diplomatic influence
No full textWhy and how does Russia engage in the arms trade? Scholars have largely focused on why Russia participates in the arms trade, often neglecting the equally crucial question of how it conducts this trade. Yet understanding the mechanisms by which Russia promotes arms sales provides deeper insights into why it does so. While many portray Russia’s arms trade as driven by economic or strategic motivations, few examine the specific tools it employs, particularly defence counter-trade, which includes non-monetary barter, counter-purchase obligations, and industrial or technological investments (offsets). This paper fills that gap by offering an eight-decade perspective on Russian arms trade practices, drawing on data and case studies to uncover a more nuanced set of motives. Russia integrates economic and political objectives in its arms trade, seeking not only to out-compete Western suppliers but also to expand or regain influence in various regions, circumvent Western-imposed sanctions, secure access to valuable resources, and sustain its military capabilities. Although barter and technological cooperation have long been part of its trade practices, Russia has only recently adopted offset practices in a systematic way. By leveraging defence counter-trade, Moscow aims to stabilise, and potentially grow, its arms exports as global conditions shift.counter-tradeJonata Anicetti acknowledges funding from the European Union through a European Research Council grant for the project Competition in the Digital Era: Geopolitics and Technology in the Twenty-First Century (CODE), under grant number 101116328.European Journal of International Securit
Improved convenience or a cyber security threat? Public acceptance of mobility-as-a-service in Great Britain
No full textThis study examines public perceptions of Mobility-as-a-Service (MaaS)—an emerging transport model that integrates multiple services such as public transit, ridesharing, and cycling into a single, user-friendly digital platform, offering travellers a comprehensive multimodal transportation solution. While MaaS is gaining traction, it remains a relatively new concept, and public understanding and acceptance are still developing. To explore this, we conducted four online focus groups in January 2024 with 24 participants from both urban and rural areas across Great Britain, including London, Greater Manchester, rural Exeter, and rural Norwich. Using the Technology Acceptance Model as a framework, we investigated factors influencing willingness to adopt MaaS, including practical concerns and digital trust. Our findings indicate that adoption is influenced by location, technological familiarity, cost, and socio-demographic factors such as age. Participants in rural areas questioned the feasibility of MaaS in their local contexts. While concerns about data privacy and cyber security were not initially raised, once prompted, they emerged as important factors shaping trust and adoption. Based on these findings, we recommend that MaaS providers prioritise data security, transparency, and effective communication to support broader adoption. Incorporating security features into the core service design is also essential for building user trust and encouraging wider acceptance.This research was part of the Mobility as a Service: Managing Cyber security Risks across Consumers, Organisations and Sectors (MACRO) project and was supported by the Engineering and Physical Sciences Research Council (EPSRC) [EP/V039164/1].Energy Research & Social Scienc
An analysis of factors that influence the spatial pattern of faecal matter flow in unsewered cities
Get PDFThe management of sanitation systems in unsewered cities in low and middle income countries is a critical issue, yet it is unclear where the risk hotspots are and where interventions should be focused. This study utilised a prototype model, developed by the authors, to map the spatial pattern of faecal flow in Rajshahi city, a secondary city in northwest Bangladesh with a population around a million. This city serves as a representative example of 60 such secondary cities in Bangladesh and hundreds more in the economically developing region in Asia, Africa and Latin America. The model relies on assumptions that carry significant uncertainties; hence, the study employed a sensitivity analysis with multiple plausible scenarios to characterise these uncertainties, aiming to identify ways to improve the model further. Five major influencing factors on the spatial pattern of faecal flow were identified: the emptying of septic tanks, the use of soak pits, and sludge removal from drains, variations in faecal matter production by building types, and the presence or absence of toilets. These factors were shown to collectively have a significant impact (almost 50 % changed) on the model outcome, depending upon the assumptions made. The study offers insights that will guide future data collection efforts by emphasising the need to understand these specific influencing factors and their spatial pattern. Consequently, this research has broader implications for urban sanitation management as well as associated public health research like wastewater surveillance, risk assessment, and disease dynamics in similar urban settings, offering insights into areas of uncertainty that need to be addressed in future modelling efforts.This work was supported by the UKRI Engineering and Physical Science Research Council (EPSRC) through a Ph.D. studentship received by the first author (M.S.S.) as part of the EPSRC Centre for Doctoral Training in Water and Waste Infrastructure and Services Engineered for Resilience (Water-WISER). EPSRC Grant No.: EP/S022066/1.Science of The Total Environmen
Artificial intelligence for prediction of shelf-life of various food products: recent advances and ongoing challenges
Get PDFBackground: Accurate estimation of shelf-life is essential to maintain food safety, reduce wastage, and improve supply chain efficiency. Traditional methods such as microbial and chemical analysis, and sensory evaluation provide reproducible results but require time and labor and may not be suitable for real-time or high-throughput applications. The integration of artificial intelligence (AI) with advanced analysis techniques offers a suitable alternative for rapid, data-driven estimation of shelf-life in dynamic storage environments. Approach and scope: The current review assesses the application of AI-based techniques such as machine learning (ML), deep learning (DL), and hybrid approaches in food product shelf life prediction. This study highlights how AI can be utilized to examine data from non-destructive testing methods like hyperspectral imaging, spectroscopy, machine vision, and electronic sensors to enhance predictive performance. The review also describes how AI-based techniques contribute to managing food quality, reduce economic losses, and enhance sustainability by ensuring optimized food distribution and reducing waste. Key findings and conclusions: AI techniques overcome conventional techniques by considering intricate, multi-sourced information capturing microbiological, biochemical, and environmental factors influencing food spoilage. Meat, dairy, fruits and vegetables, and beverage case studies illustrate AI techniques' superiority in real-time monitoring and quality assessment. It also identifies limitations such as data availability, model generalizability, and computational cost, constraining extensive applications. Cloud and Internet of Things (IoT) platform integration into future applications has to be considered to enable real-time decision-making and adaptive modeling. AI can be a paradigm-changing tool in food industries with intelligent, scalable, and low-cost interventions in food safety, waste reduction, and sustainability.This work was supported by the National Centre of Excellence for Food Engineering, Sheffield Hallam University.Trends in Food Science & Technolog
Advanced diagnostics of aircraft structures using automated non-invasive imaging techniques: a comprehensive review
Get PDFThe aviation industry currently faces several challenges in inspecting and diagnosing aircraft structures. Current aircraft inspection methods still need to be fully automated, making early detection and precise sizing of defects difficult. Researchers have expressed concerns about current aircraft inspections, citing safety, maintenance costs, and reliability issues. The next generation of aircraft inspection leverages semi-autonomous and fully autonomous systems integrating robotic technologies with advanced Non-Destructive Testing (NDT) methods. Active Thermography (AT) is an example of an NDT method widely used for non-invasive aircraft inspection to detect surface and near-surface defects, such as delamination, debonding, corrosion, impact damage, and cracks. It is suitable for both metallic and non-metallic materials and does not require a coupling agent or direct contact with the test piece, minimising contamination. Visual inspection using an RGB camera is another well-known non-contact NDT method capable of detecting surface defects. A newer option for NDT in aircraft maintenance is 3D scanning, which uses laser or LiDAR (Light Detection and Ranging) technologies. This method offers several advantages, including non-contact operation, high accuracy, and rapid data collection. It is effective across various materials and shapes, enabling the creation of detailed 3D models. An alternative approach to laser and LiDAR technologies is photogrammetry. Photogrammetry is cost-effective in comparison with laser and LiDAR technologies. It can acquire high-resolution texture and colour information, which is especially important in the field of maintenance inspection. In this proposed approach, an automated vision-based damage evaluation system will be developed capable of detecting and characterising defects in metallic and composite aircraft specimens by analysing 3D data acquired using an RGB camera and a IRT camera through photogrammetry. Such a combined approach is expected to improve defect detection accuracy, reduce aircraft downtime and operational costs, improve reliability and safety and minimise human error.This research was supported and funded by the British Engineering and Physical Sciences Research Council (EPSRC), grant number EP/T518104/1.Applied Science
Energy optimization strategies for automatic tiltrotor electric vertical takeoff and landing aircraft
No full textJournal of Guidance, Control, and Dynamic
Fault diagnosis across aircraft systems using image recognition and transfer learning
Get PDFThe data presented in this study are available on request from the corresponding author. The data are not publicly available due to the copyright of the simulation software used to generate them.With advances in machine learning, the fault diagnosis of aircraft systems is becoming more efficient and accurate, which makes condition-based maintenance possible. However, current fault diagnosis algorithms require abundant and balanced data to be trained, which is difficult and expensive to obtain for aircraft systems. One solution is to transfer the diagnostic knowledge from one system to another. To achieve this goal, transfer learning was explored, and two approaches were attempted. The first approach uses relational similarity between the source and target domain features to enable the transfer between two different systems. The results show it only works when transferring from the fuel system to ECS but not to APU. The second approach uses image recognition as the intermediate domain linking the distant source and target domains. Using a deep network pre-trained with fuel system images or the ImageNet dataset finetuned with a small amount of target system data, an improvement in accuracy is found for both target systems, with an average of 6.90% in the ECS scenario and 5.04% in the APU scenario. This study outlines a pioneering approach that transfers knowledge between completely different systems, which is a rare transfer learning application in fault diagnosis.Applied Science
Search and rescue operations in wildfires using unmanned aerial vehicles: a multi-agent deep reinforcement learning approach
No full textWildfires pose major challenges to natural ecosystems and smart living due to their destructive nature. Unmanned Aerial Vehicles (UAVs) or drones have been used to support fire fighter in identifying vulnerable areas and detecting people who need assistance. Most of the current solutions use path planning approaches under simple and deterministic environments that fail to model the dynamic nature of fire. Furthermore, the localisation of victims is assumed to be known which is unrealistic in disaster-like scenarios. To alleviate this issue, this paper proposes a novel search and rescue (SAR) application using drones. A multi-agent deep Q-network is designed to train a fleet of UAVs to search for people and evacuate them in a wildfire scenario. A realistic forest environment is designed that considers variations in vegetation and fire propagation. This helps to challenge RL algorithms to be more adaptive to changes in the environment due to the evolution of fire. Extensive simulation experiments are conducted to show the advantages and effectiveness of the proposed approach.Neurocomputin
Immobilization of laccases from Pycnoporus sanguineous on magnetized carbon nanofibers for the degradation of psychiatric drugs: venlafaxine and carbamazepine
No full textThe COVID-19 pandemic has significantly increased the consumption of psychiatric medications, such as venlafaxine (VFX) and carbamazepine (CBZ), leading to their accumulation in wastewater and subsequent environmental concerns. These compounds are classified as emerging pollutants, presenting challenges for conventional wastewater treatment systems. This study, faced the immobilization of laccases derived from a native strain of Pycnoporus sanguineus CS43, isolated from northeastern Mexico, onto magnetically modified carbon nanofibers (mCNF) to enhance degradation efficiency, enzyme stability, and reusability. The enzyme immobilization on mCNF resulted on high loading value of 73.24%. The performing of the degradation assays revealed that this innovative system achieved 51.51% and 57.14% removal of VFX and CBZ respectively within just 4 h at pH 5 and 25°C. A remarkable stability of the system was demonstrated retaining between 70% and 100% the enzyme activity on the nanomaterial. After 28 days of storage, the nanobiocatalysts system retained 74.50% of their initial activity. These results highlight the potential of mCNF as an effective support for laccase immobilization, providing a sustainable and efficient strategy for the bioremediation of psychiatric drug pollutants in aquatic environments.This research was partially supported by Secretaria de Ciencia, Humanidades, Tecnología e Inovación (SECIHTI) and Tecnologico de Monterrey. Prof. Parra-Saldivar is funding by Research England, UK.Results in Engineerin
Revolutionizing power electronics design through large language models: applications and future directions
No full textThe design of electronic circuits is critical for a wide range of applications, from the electrification of transportation to the Internet of Things (IoT). It demands substantial resources, is time-intensive, and can be highly intricate. Current design methods often lead to inefficiencies, prolonged design cycles, and susceptibility to human error. Advancements in artificial intelligence (AI) play a crucial role in power electronics design by increasing efficiency, promoting automation, and enhancing sustainability of electrical systems. Research has demonstrated the applications of AI in power electronics to enhance system performance, optimization, and control strategy using machine learning, fuzzy logic, expert systems, and metaheuristic methods. However, a review that includes the recent AI advancements and potential of large language models (LLMs) like generative pre-train transformers (GPT) has not been reported. This paper presents an overview of applications of AI in power electronics (PE) including the potential of LLMs. The influence of LLMs-AI on the design process of PE and future research directions is also highlighted. The development of advanced AI algorithms such as pre-train transformers, real-time implementations, interdisciplinary collaboration, and data-driven approaches are also discussed. The proposed LLMs-AI is used to design parameters of high-frequency wireless power transfer (HFWPT) using MATLAB as a first case study, and high-frequency alternating current (HFAC) inverter using PSIM as a second case study. The proposed LLM-AI driven design is verified based on a similar design reported in the literature and Wilcoxon signed-rank test was conducted to further validate the result. Results show that the LLM-AI driven design based on the OpenAI foundation model has the potential to streamline the design process of power electronics. These findings provide a good reference on the feasibility of LLMs-AI on power electronic design.The Energy Research Lab (ERL) and Cranfield University have sponsored this work.Computers and Electrical Engineerin