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This study introduces an innovative energy harvesting system designed for industrial applications such as fluid pipelines, air conditioning ducts, sewer systems, and subsea oil pipelines. The system integrates magneto-electric flow coupling and utilizes a dynamic vibration absorber (DVA) to mitigate the vibrations induced by fluid flow while simultaneously harvesting energy through magnetic dipole–dipole interactions in a vibration energy harvester (VEH). The theoretical models, based on Hamilton's Principle and the Biot–Savart Law, were validated through comprehensive experiments. The results indicate the superior performance of the small-magnet system over the large-magnet system in both damping and power generation. The study analyzed the frequency response and energy conversion efficiency across different parameters, including the DVA mass, spring constant, and placement location. The experimental findings demonstrated significant vibration reduction and increased voltage output, validating the theoretical model. This research offers new avenues for energy harvesting systems in pipeline infrastructures, potentially enhancing energy efficiency and structural integrity. [ABSTRACT FROM AUTHOR]

This study investigates the application of deep learning models—specifically Deep Neural Networks (DNN), Long Short-Term Memory (LSTM), and Long Short-Term Memory Neural Networks (LSTM-NN)—to predict panel flutter in aerospace structures. The goal is to improve the accuracy and efficiency of predicting aeroelastic behaviors under various flight conditions. Utilizing a supersonic flat plate as the main structure, the research integrates various flight conditions into the aeroelastic equation. The resulting structural vibration data create a large-scale database for training the models. The dataset, divided into training, validation, and test sets, includes input features such as panel aspect ratio, Mach number, air density, and decay rate. The study highlights the importance of selecting appropriate hidden layers, epochs, and neurons to avoid overfitting. While DNN, LSTM, and LSTM-NN all showed improved training with more neurons and layers, excessive numbers beyond a certain point led to diminished accuracy and overfitting. Performance-wise, the LSTM-NN model achieved the highest accuracy in classification tasks, effectively capturing sequential features and enhancing classification precision. Conversely, LSTM excelled in regression tasks, adeptly handling long-term dependencies and complex non-linear relationships, making it ideal for predicting flutter Mach numbers. Despite LSTM's higher accuracy, it required longer training times due to increased computational complexity, necessitating a balance between accuracy and training duration. The findings demonstrate that deep learning, particularly LSTM-NN, is highly effective in predicting panel flutter, showcasing its potential for broader aerospace engineering applications. By optimizing model architecture and training processes, deep learning models can achieve high accuracy in predicting critical aeroelastic phenomena, contributing to safer and more efficient aerospace designs. [ABSTRACT FROM AUTHOR]

This technical note numerically and experimentally studies a vibration energy harvester (VEH) consisting of a set of two parallel elastic steel sheets (ESSs) and piezoelectric patches (PZTs) in pipe flow. The roots of the elastic steel sheets are fixed on the base with the PZTs to form a double inverted flag vibration energy harvesting system (DIF-VEHS). In this note, a semi-circular receiving device (receiver) was added to the free end of the elastic steel, and a cylinder was installed upstream to generate a periodic oscillating flow field in the pipeline to obtain better electric energy generation efficiency. This study reveals the effect of different factors on the energy harvesting system, such as the distance between the ESSs, the diameter of the cylinder, etc. This study uses ANSYS software to simulate the fluid–structure interaction vibration of ESSs to determine the feasibility of this design. An experimental setup is then implemented to find the most effective combination of factors for the system. The results of this study show that with all parameters configured properly, the electric energy generation reaches a maximum average value of 1.6657 V per minute. In the future, such devices could be installed in sewers, pipes or rivers, allowing the flow energy of the fluid to be recycled to generate more energy. [ABSTRACT FROM AUTHOR]

Taiwan's prolonged process of health care reform may provide lessons for the US. Originally a market-based system similar to that in the US, Taiwan's health care industry has been regulated more and more by the government since the early 1980s. In 1995, a plan for universal health insurance is to be implemented. Previously, only 54% of Taiwanese were insured under three government programs. Combining these plans into one should lower costs and streamline administration. The universal coverage plan includes substantial co-payments by patients. This is seen as one way to limit utilization and control costs. In contrast to the US, however, Taiwan does not have a large private health insurance industry to contend with. Slow but careful progress in health care reform may make the political and social transition to universal coverage easier to achieve than a major system overhaul.