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Understanding migration-related transnational organised crime in Indonesian waters
No full textResearch conducted in collaboration between IOM Indonesia and Coventry University in 2015 and 2016, examined the way in which the Indonesian state understood and utilised the concept of maritime security1. As part of this research, a Training Needs Analysis of key Indonesian state maritime security actors was conducted, examining how Indonesia's maritime security capacity can be improved. In this framework, research participants identified ‘smuggling and trafficking of persons’ by sea as one of the top three predominant threats to Indonesia’s maritime security.Building on the previously conducted research, the project titled “Transcending Borders – Strengthening Coordination, Detection and Management of Migration-Related Transnational Organised Maritime Crime in Indonesia” seeks to further understand how this pluralism of threats and key actors have evolved since then, and how contemporary smuggling and trafficking crimes manifest themselves within the Indonesian archipelago. The research was conducted from January 2024 to July 2025, and it was funded by the Bureau of International Narcotics and Law Enforcement (INL), the U.S. Department of State. This report presents its findings, particularly focused on the key Indonesian maritime security stakeholders’ understanding of migration-related TOC at sea and their top priorities in addressing these crimes. The findings stem from the analysis of qualitative primary data collected from relevant research participants, through 27 semi-structured interviews between March and October 2024 in Jakarta, Batam (Riau Islands) and Ambon (Maluku Islands) and a focus group discussion with representatives from 13 Indonesian Institutions (both Government Ministries and Law Enforcement agencies), convened and delivered in Jakarta in Sep 2024
Crushing performance of multi-celled tubes fabricated by bending metal sheets
No full textNovel folded multi-celled tubes (FMTs) fabricated by bending metal sheets were designed and introduced in this paper to achieve an effective balance between a lower initial peak force (IPF) and ease of manufacturing.The base computer model of the FMTs was corrected using physical experiment data. IPF, energy absorption (EA), mean crushing force (MCF) and specific energy absorption (SEA) served as crashworthiness indicators.Additionally, the effects of thickness, boundary conditions, welding configurations, and cross-sectional shapes on the energy absorption of FMTswere investigated, followed by a comparison of their crashworthiness with that of conventional multi-celled tubes (CMTs). The results demonstrated that FMTs outperform CMTs, achieving lower IP of up to 9.53% and offering improved manufacturability. Furthermore, FMTs featuring square-circle cross-sections demonstrated superior crashworthiness than the FMT with square-square cross-section shape. A multi-objective optimization method based on the NSGA-II algorithm was employed to further enhance crashworthiness. The optimal FMT configurations demonstrated energy absorption improvements of up to 367.05%, confirming the robustness and effectiveness of the optimization method. With their controllable collapse behavior, lower IPF, and versatile manufacturability, FMTs hold great promise for diverse engineering applications
Machine Learning and Data Analytics in the Gig Economy
No full textThe integration of machine learning and data analytics within the dynamic landscape of the gig economy and big data applications has emerged as a pivotal force reshaping the way businesses operate and individuals engage in work. This chapter explores the intersection of these technological advancements and their profound implications across various sectors.In the contemporary gig economy, characterized by the prevalence of short-term contracts and freelance work arrangements, the utilization of machine learning algorithms and data analytics tools has become instrumental in optimizing resource allocation, enhancing efficiency, and facilitating decision-making processes. Through the analysis of vast datasets generated by gig platforms, businesses can gain valuable insights into consumer behavior, market trends, and workforce dynamics, enabling them to tailor their strategies and offerings to meet evolving demands.Moreover, the proliferation of big data in the digital era has presented both opportunities and challenges for organizations seeking to harness its potential. Machine learning algorithms play a crucial role in extracting actionable intelligence from the vast volumes of unstructured data generated by online transactions, social media interactions, and the Internet of Things (IoT) devices. By leveraging predictive analytics and pattern recognition techniques, businesses can uncover hidden patterns, detect anomalies, and forecast future trends with unprecedented accuracy.Furthermore, the application of machine learning algorithms in big data analytics has revolutionized various sectors, including finance, healthcare, transportation, and marketing. From fraud detection and risk management in financial services to personalized healthcare and predictive maintenance in manufacturing, the integration of advanced analytics techniques has enabled organizations to unlock new sources of value, drive innovation, and gain a competitive edge in the marketplace. However, alongside the potential benefits, the widespread adoption of machine learning and data analytics poses significant ethical, legal, and societal implications. Issues related to data privacy, algorithmic bias, and job displacement warrant careful consideration to ensure that the benefits of technological innovation are equitably distributed and that safeguards are in place to mitigate potential risks.In conclusion, this book chapter offers a comprehensive overview of the transformative impact of machine learning and data analytics in the gig economy and big data applications, highlighting both the opportunities and challenges that lie ahead in this rapidly evolving landscape. Through case studies, empirical research, and theoretical insights, it provides valuable insights for researchers, practitioners, and policymakers seeking to navigate the complexities of the digital age
Distributed Coordination for Heterogeneous Non-Terrestrial Networks
Get PDFTo achieve global coverage and ubiquitous connectivity, the non-terrestrial network (NTN) has been regarded as a key enabler in the sixth generation (6G) network, which includes uncrewed aerial vehicles (UAVs), high-altitude platforms (HAPs), and satellites. Since the unique characteristics of various NTN platforms strongly affect their implementation and lead to a highly dynamic and heterogeneous NTN scenario, achieving distributed coordination remains an important research direction. However, the explicit and systematic analysis of the individual layers' challenges and corresponding distributed coordination solutions in heterogeneous NTNs has not been proposed yet. Therefore, in this article, we summarize the unique characteristics of each NTN platform, identify communication challenges within individual layers, and propose potential delay-tolerant or delay-sensitive coordinated solutions accordingly. We further analyse the feasibility of leveraging multi-agent deep reinforcement learning (MADRL) algorithms to achieve the proposed coordinated solutions. Finally, we present a case study of the joint scheduling and trajectory optimization problem in heterogeneous NTN, where a two-timescale multi-agent deep deterministic policy gradient (TTS-MADDPG) algorithm is developed to validate the effectiveness of distributed coordination
Cyber-Physical Integration and Experimental Validation of Impeller Failures Using IoT-Enabled Digital Twin Framework
Get PDFThis interdisciplinary study investigates impeller failure using a combined digital and experimental approach, establishing a proof of concept for cyber-physical integration. First, a CAD model was developed and its structural integrity is validated using Finite Element Analysis (FEA) to ensure the impeller could withstand operational loads and dynamic stresses, following the methodology outlined in [1]. Next, an IoT-enabled digital twin framework was implemented with Arduino-based sensors (temperature, humidity, vibration) to monitor 3D-printed impellers made from 316L stainless steel and AlSi10Mg aluminium. The sensors were integrated with a custom test rig driven by a motor capable of 10,000 rpm, with data acquired via analog/digital interfaces and visualized in Node-RED, streaming in real time to an IoT cloud platform. Impeller experiments ran for over 80 hours and were tested under two corrosive conditions: (i) engine oil (5W-30) and (ii) saltwater. SEM/EDS analysis revealed carbon deposits on oil-exposed samples and aluminium oxide on saltwater-exposed ones, while further SEM imaging showed pitting and corrosion. Alicona surface roughness tests confirmed degradation under dynamic loads. Preliminary real-time monitoring demonstrated the of predictive maintenance alerts, though fullscale validation remains future work. Overall, the developed framework provides a robust basis for physical testing with digital representation, offering strong potential for predictive maintenance
Human-Centred Classification of Remote Operation Intervention Scenarios for Automated Vehicles
Get PDFThe deployment of automated vehicles (AVs) offers substantial societal benefits but faces significant challenges, particularly in complex urban environments. Remote operations (ROs) enabled safety interventions serve as a critical intermediary, allowing human operators to intervene when AVs encounter limitations. However, to inform RO workstation design and understand human intervention capabilities, real-world scenario data is critical. To address this gap, we conducted semi-structured interviews with 13 local safety experts at the Birmingham National Exhibition Centre (NEC), UK, resulting in a catalogue of 105 functional scenarios. We identified prevalent scenario types, AV-related challenges, and scenario complexity. These findings inform RO workstation and human machine interface (HMI) design and highlight the need for realworld scenario generation to support RO capability assessment. This study contributes to a framework for enhancing human-AV interactions, understanding RO safety requirements, and advancing ROs.</p
A Study on 3D Printed CFRP-Metal Sandwich Structures for Hydrogen Transportation and Storage in Aerospace and Automotive Systems
Get PDFThis study investigates the mechanical performance of 3Dprinted Carbon Fiber Reinforced Polymer (CFRP)-metal sandwich structures, focusing on infill pattern, infill ratio, and build orientation. The work primarily targets lightweight applications in hydrogen storage for aerospace and automotive sectors. Tensile and flexural tests were first conducted on non-sandwich specimens based on an earlier study [1], considering (i) infill patterns - Gyroid and Triangular and (ii) infill ratios -30%, 40%, and 52%. In tensile tests, strength increased with infill ratio, with Triangular infill consistently outperforming Gyroid, peaking at 863 N (Triangular 52%) versus 777.5 N (Gyroid 52%). Lower displacement in Triangular indicated higher stiffness. Flexural tests on non-sandwich specimens showed Gyroid 40% achieving the highest load (644.85 N), while Triangular 52% exhibited steady strength gains with increasing density. Selected configurations were further tested in sandwich structures (CFRP–stainless steel with nickel coating), where Gyroid 40% demonstrated the highest load-bearing capacity (1008.55 N), followed by Triangular 52% (717.09 N). Notably, Gyroid 52% showed the greatest extension (14.38 mm) despite lower strength, indicating enhanced ductility. Overall, the results highlight a trade-off between stiffness and compliance, suggesting Triangular infill for high-strength, rigid components and Gyroid for balanced flexural performance. Overall, this work provides an impactful study on optimizing CFRP-metal hybrid designs for hydrogen storage system
Curating a Corpus:A Three-Phase Model
No full textWe describe in this Research Note processes and protocols for curating a corpus of texts for analysis, from collection of data to readiness for analysis. We offer example case studies working with texts in different genres and languages, and using different tools, to illustrate general principles for corpus curation. Rather than a comprehensive guide for researchers interested in corpus linguistics methods, we offer a conversational starting point, supplementing our overview of three phases (collection, cleaning, and pre-processing) with authentic experiences from our own diverse research. Further, we reflect on the pedagogical implications associated with corpus linguistics, as well as the challenges and next steps in corpus curation and analysis in the age of generative AI. Our experiences show how common curation phases can be applied to different studies and contexts, and the considerations that arise when doing so.</p