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A multi-view analysis of 6G system of systems:A SE perspective
No full text6G communication systems and related technologies are announced as the next leap and transformative step to advance information, communication and automation technologies. Indeed, several publications already explored challenges, opportunities and prospects for 6G related technologies. However, most of such analyses mainly focus on technological aspects, related to either telecommunication engineering or to architectural aspects of 6G-based technological systems. There is still a lack of a multi-perspective analysis, one that places 6G into a wider socio-technical context, by confronting expected (i.e. almost known) 6G technological evolutions to less explored challenges related to deployment and managerial implications, societal transformations, necessary human talent training and competence upscaling. This paper contributes with a multiperspective analysis by adopting a systems engineering perspective to analyze the interplay between technology, society, and education in the context of 6G
Lagrangian flow statistics in experimental homogeneous isotropic turbulence
No full textWe report on Lagrangian flow statistics from experimental measurements of homogeneous isotropic turbulence. The investigated flow is driven by 12 impellers inside an icosahedral volume. Seven impeller rotation rates are considered resulting in seven Reynolds numbers with 205 ≤ Re λ ≤ 602 . We perform high-speed imaging using three cameras to record a total of 8.2 × 10 6 frames, and using high resolution three dimensional (3D) particle tracking velocimetry position, velocity, and acceleration of particle tracks are obtained in the vicinity of the center of the device. From these tracks, we obtain the Eulerian and Lagrangian flow statistics, mainly based on second-order structure functions and autocorrelation functions. The universal constants C 0 * (for the Lagrangian second order structure function), C ϵ (for the energy injection rate), and a 0 (for the acceleration fluctuations) are determined, as well as all the relevant Lagrangian and Eulerian flow time scales. Analytical relations between these constants and time scales are experimentally verified.</p
Revealing the project and asset management divide:Why infrastructure agencies struggle with IT transformation
Full text linkAmid aging infrastructures, rapid urbanization, and the impacts of climate change, infrastructure agencies are under pressure to modernize their asset information management to enhance the future provision of infrastructure services. However, transforming the current information technology (IT) landscape presents significant challenges. Drawing from three comprehensive, practice-oriented case studies, this PhD dissertation illuminates what these challenges are and why they arise. It reveals the divide between organizing work in infrastructure projects, on the one hand, and executing asset management, on the other hand. Ultimately, findings from this dissertation can guide infrastructure agencies in designing strategies that fully consider the complexities and magnitude of transforming from project-based organizations to data-driven asset managers
Salt-ring in your pasta pan:Morphology of particle cloud deposit
No full textWhen added to a pot of boiling pasta, a handful of salt grains can create a fascinating and varied pattern of deposits. In this study, we experimentally investigate this phenomenon by examining the deposits, focusing on the radial distribution of clusters of settling spherical particles in a quiescent viscous fluid at an intermediate Reynolds number. We demonstrate that the particle diameter d, the settling height H, and the injected particle volume V inj all influence the particles' radial spread. Additionally, the injection method significantly impacts the deposit's final morphology. The resulting deposit structure emerges from the sedimentation history of the particle cloud, reflecting a complex interplay of various physical mechanisms.</p
Spatial Robust Whole-Body Dynamic Trajectory Optimization of a Lower-Limb Exoskeleton
No full textThe goal of this paper is to design spatial (3D) robust reference trajectories for lower limb exoskeletons through optimization. Robustness is defined as the magnitude of the smallest force applied at the Center of Mass that cannot be rejected without violating joint torque or ground reaction force bounds. A larger robustness margin in the reference trajectory increases the capability of the controller to reject perturbations. A trajectory optimization problem was augmented by adding the maximization of this robustness metric to it. The augmented trajectory optimization is then employed to design dynamic trajectories for three different tasks. We demonstrated that our method increases the robustness metric value by comparing the results from the proposed method with a nominal trajectory optimization that does not take robustness into account. Moreover, the designed trajectories were implemented on a lower limb exoskeleton with a paraplegic user to show the feasibility of the proposed method. The user managed successfully to walk on level ground, perform side-stepping, and ascend stairs.</p
AI-Powered API for Brain Tumour Classification: A Deep Learning Approach to Accessible Medical Imaging
No full textThis paper presents a deep learning-based API designed for automated brain tumour classification from MRI scans, addressing the need for accessible diagnostic tools in clinical and resource-limited environments. Leveraging two state-of-the-art models, YOLO for real-time object detection and Roboflow for multi-label image classification, the study develops and evaluates an AI-powered diagnostic API implemented with FastAPI. The models were trained on a publicly available dataset containing glioma, meningioma, pituitary tumours, and non-tumorous images. Evaluation metrics include accuracy, validation accuracy, and confusion matrices. Roboflow achieved superior classification accuracy (96.1%) compared to YOLO (84.72%), while YOLO demonstrated faster inference, making it ideal for real-time use. The API ensures ease of deployment, robust handling of low-quality inputs, and compatibility with various clinical setups. Ethical considerations such as data privacy and model transparency were also addressed. The study concludes that combining deep learning with accessible APIs can significantly enhance diagnostic support, but stresses the importance of explainability, regulatory compliance, and broader dataset diversity for full-scale clinical integration.<br/
Resilient Time Synchronisation for Aerial Swarms by Distributed Graph Neural Networks
No full textAerial swarms consisting of multiple Unmanned Aerial Vehicles (UAVs) have been applied across various domains. Time synchronisation is important for swarm wireless networks. However, most studies assume that UAV clocks are synchronised, without addressing how synchronisation is achieved. Moreover, existing control techniques are typically designed in a centralised manner with a known topology, limiting their applicability to large swarms or those with dynamic wireless networks caused by high mobility or communication link failures resulting from jamming attacks. These limitations may lead to the instability of the controllers, or even the downtime of the entire swarm system, particularly under the communication link failures. Therefore, in this work, we propose leveraging Graph Neural Networks (GNNs) to achieve resilient clock synchronisation among UAVs. First, we integrate the heat kernel into the graph neural network, allowing it to retain low-frequency graph signals while attenuating high-frequency components. This is consistent with the aim of time synchronisation, which is to ensure that the states of all the clocks are the same, corresponding to low-frequency graph signals. Meanwhile, we introduce a distributed GNN architecture with low communication overhead, in contrast to existing decentralised GNNs that rely on fully-connected networks. Through adversarial imitation learning, our GNN-based control policies achieve similar synchronisation performance without requiring re-training when scaled to large swarms, as compared to the centralised controller using fully-connected wireless networks. Once trained, the proposed GNN-based control policies are also resilient to varying wireless networks, including temporary or permanent communication link failures, and can maintain synchronisation even when the swarm is split into two disconnected parts
Energy-Efficient Four-Mode Electromagnetic Mini Valve with Single-Coil Drive:Modular Design and Application in Pneumatic Soft Actuators
No full textIn minimally invasive surgery, pneumatic soft actuators have gained significant attention due to their promising applications. Valves, which are critical components in pneumatic driving systems, typically operate in only two modes in current research. In multi-actuator systems, this limitation results in a large number of valves and a bulky driving system, which restricts the system’s portability. In this study, we report a four-mode, multi-stable, energy-efficient electromagnetic mini valve. A coil serves as the driving source. The valve has three outlets, enabling it to control up to three actuators independently. The moving components include a cylindrical magnet and a ring magnet. They are concentrically arranged and can only move along the axial direction. These magnets can realize four distinct positions based on the electromagnetic force and their mutual magnetic force. Moreover, the multi-stable design reduces energy consumption. The two cores make the magnets stable when no current is applied. The valve exhibits a shortest response time of 4.75 ms and an energy cost of 0.007 J. It can handle a maximum flow rate of 9.7 L/min at a pressure of 200 kPa, with a maximum back pressure of 100 kPa. Its energy-efficient and four-operating-mode characteristics prove its potential in pneumatic soft actuator systems. A modular design by connecting multiple mini valves reduces the number of pumps and valves, thereby lowering the overall size and weight of pneumatic driving systems. Note to Practitioners—Pneumatic soft actuators are widely used in automation systems because they are flexible and safe. However, controlling multiple actuators often needs many valves, making the system large and heavy. This work introduces a small valve with four operating modes. It can control up to three actuators by itself. The valve holds its position without continuous power, which saves energy. It also responds quickly and can deliver a high airflow. By connecting several of these valves, users can build simpler and lighter pneumatic systems. This valve is useful for portable automation devices, medical robots, and wearable systems. In the future, the valve design could be connected to smart controllers for more advanced automatic control
Progress in Medicine
No full textThis chapter reflects on the question, “Is medicine getting better?” The answer to the question depends, first, on what the core aims and competences of medicine are taken to be. This chapter discusses two positions on what the core aims and competences of medicine are. According to “medical nihilism,” intervening on diseases is the main aim of medicine. According to the “inquiry model of medicine,” the core competences of medicine are understanding and predicting diseases. Second, from whose perspective should the progress of medicine be evaluated? No matter whether one accepts medical nihilism or the inquiry model of medicine, it is important to consider how biases in medical research and clinical practice influence the progress of medicine. The chapter concludes by considering suggestions for promoting better medicine