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Flow-induced vibration is a canonical issue in various engineering fields, leading to fatigue or immediate damage to structures. This paper numerically investigates flow-induced vibrations of a cylinder interacting with the wake of another cylinder at a Reynolds number Re = 150. It sheds light on the effects of mass ratio m*, damping ratio, and mass-damping ratio m*ζ on vibration amplitude ratio A/D at different reduced velocities Ur and cylinder spacing ratios L/D = 1.5 and 3.0. A couple of interesting observations are made. The m* has a greater influence on A/D than ζ although both m* and ζ cause reductions in A/D. The m* effect on A/D is strong for m* = 2–16 but weak for m* > 16. As opposed to a single isolated cylinder case, the mass-damping m*ζ is not found to be a unique parameter for a cylinder oscillating in a wake. The vortices in the wake decay rapidly at small ζ. Alternate reattachment of the gap shear layers on the wake cylinder fuels the vibration of the wake cylinder for L/D = 1.5 while the impingement and switch of the gap vortices do the same for L/D = 3.0.

The concept of the Internet of Medical Things brings a promising option to utilize various electronic health records stored in different medical devices and servers to create practical but secure clinical decision support systems. To achieve such a system, we need to focus on several aspects, most notably the usability aspect of deploying it using low-end devices. This study introduces one such application, namely FedSepsis, for the early detection of sepsis using electronic health records. We incorporate several cutting-edge deep learning techniques for the prediction and natural-language processing tasks. We also explore the multimodality aspect for the better use of electronic health records. A secure distributed machine learning mechanism is essential to building such a practical internet of medical things application. To address this, we analyze two federated learning techniques. Moreover, we use two different kinds of low-computational edge devices, namely Raspberry Pi and Jetson Nano, to address the challenges of using such a system in a practical setting and report the comparisons. We report several critical system-level information about the devices, namely CPU utilization, disk utilization, process CPU threads in use, process memory in use (non-swap), process memory available (non-swap), system memory utilization, temperature, and network traffic. We publish the prediction results with the evaluation metrics area under the receiver operating characteristic curve, the area under the precision–recall curve, and the earliness to predict sepsis in hours. Our results show that the performance is satisfactory, and with a moderate amount of devices, the federated learning setting results are similar to the single server-centric setting. Multimodality provides the best results compared to any single modality in the input features obtained from the electronic health records. Generative adversarial neural networks provide a clear superiority in handling the sparsity of electronic health records. Multimodality with the generative adversarial neural networks provides the best result: the area under the precision–recall curve is 96.55%, the area under the receiver operating characteristic curve is 99.35%, and earliness is 4.56 h. FedSepsis suggests that incorporating such a concept together with low-end computational devices could be beneficial for all the medical sector stakeholders and should be explored further.

The impact of Strouhal number St (= 0.1–1.0), Reynolds number Re (= 50–2000) and dimensionless wavelength λ (= 0.5–2.0) on the hydrodynamic performance of a travelling wavy foil of a constant length is extensively investigated. The relationship of time-mean thrust with St, Re and λ is presented, suggesting that the propulsive force increases with increasing St, Re and λ. As such, the drag–thrust boundary advances as these parameters increase. A shorter λ makes the thrust steadier while a longer λ enhances the maximum instantaneous thrust. The latter is beneficial for prey to escape from a predator. The fluid added mass caused by the foil oscillation increases with St and λ but declines with Re (<500). Seven types of wake structures produced by the foil are identified, discussed and connected to thrust generation, showing how St, Re and λ affect the fluid dynamics, wake transition, vortex strength, wake jet, velocity, added mass, added damping, power input, efficiency and pressure profiles. The outcome of this work renders a physical basis for understanding the swimming of aquatic animals.

This paper presents a systematic study of the cross-flow-induced vibration on a spring-supported circular cylinder of diameter $D$ placed in the wake of a fixed cylinder of smaller diameter $d$ . The ratios $d/D$ and $L/d$ are varied from 0.2 to 1.0 and from 1.0 to 5.5, respectively, where $L$ is the distance between the centre of the upstream cylinder to the forward stagnation point of the downstream cylinder. Extensive measurements are conducted to capture the cylinder vibration and frequency responses, surface pressure, shedding frequencies and flow fields using laser vibrometer, hot-wire, pressure scanner and particle image velocimetry techniques. Six distinct flow regimes are identified. It has been found that a violent vibration may erupt for the spring-supported cylinder, and its dependence on $d/D$ and $L/d$ is documented. A careful examination and analysis of the flow structure, along with the simultaneously captured pressure distribution around and vibration of the downstream cylinder, cast light upon the mechanisms behind this vibration and its sustainability. The roles of added mass, flow-induced damping and physical aspects in the process of initiating the vibration are discussed in detail.

Internet of Medical Things (IoMT) provides an excellent opportunity to investigate better automatic medical decision support tools with the effective integration of various medical equipment and associated data. This study explores two such medical decision-making tasks, namely COVID-19 detection and lung area segmentation detection, using chest radiography images. We also explore different cutting-edge machine learning techniques, such as federated learning, semi-supervised learning, transfer learning, and multi-task learning to explore the issue. To analyze the applicability of computationally less capable edge devices in the IoMT system, we report the results using Raspberry Pi devices as accuracy, precision, recall, Fscore for COVID-19 detection, and average dice score for lung segmentation detection tasks. We also publish the results obtained through server-centric simulation for comparison. The results show that Raspberry Pi-centric devices provide better performance in lung segmentation detection, and server-centric experiments provide better results in COVID-19 detection. We also discuss the IoMT application-centric settings, utilizing medical data and decision support systems, and posit that such a system could benefit all the stakeholders in the IoMT domain.

The growing demand for renewable energy has underscored the importance of wind power, with wind turbines playing a pivotal role in sustainable electricity generation. However, wind turbine blades are exposed to various challenges, particularly flow-induced vibrations (FIVs), including vortex-induced vibrations, flutter, and galloping, which significantly impact the performance, efficiency, reliability, and lifespan of turbines. This review presents an in-depth analysis of wind turbine blade technology, covering the fundamental principles of operation, aerodynamic characteristics, material selection, and failure mechanisms. It examines the effects of these vibrations on blade integrity and turbine performance, highlighting the need for effective vibration suppression techniques. The paper also discusses current advancements in maintenance strategies, including active and passive vibration control methods, sensor networks, and drone-based inspections, aimed at improving turbine reliability and reducing operational costs. Furthermore, emerging technologies, such as artificial intelligence (AI)-driven prognostic assessments and novel materials for vibration damping, are explored as potential solutions to enhance turbine performance. The review emphasizes the importance of continued research in addressing the challenges posed by FIVs, particularly for offshore turbines operating in harsh environments.

Topological field-effect transistors (TFETs) based on two-dimensional materials have emerged as promising candidates for low-power electronic applications. In this study, we explore the electronic and transport properties of a bismuthene nanoribbon TFET using density functional theory combined with the nonequilibrium Green’s function formalism. We systematically examine the effects of edge passivation, channel length variation, and vacancy defects on the device’s transfer characteristics. Our results reveal that edge passivation mitigates trivial edge states, thereby enhancing robustness against structural defects and reducing the threshold voltage. Moreover, increasing the channel length effectively suppresses the tunneling leakage current during the off state, leading to an improvement in the subthreshold swing. We also evaluate key performance metrics—such as on/off ratio, capacitance, delay time, and dynamic energy dissipation—and demonstrate compliance with the IRDS standards for 10 nm gate length technology. This work highlights the significance of topological features in device performance and offers practical insights for designing next-generation energy-efficient nanoelectronic devices.