Energy harvesting has been attracting attention as a technology that is capable of replacing or supplementing a battery with the development of various mobile electronics. In environments where stable electrical supply is not possible, energy harvesting technology can guarantee an increased leisure and safety for human beings. Harvesting with several watts of power is essential for directly driving or efficiently charging mobile electronic devices such as laptops or cell phones. In this study, we reviewed energy harvesting technologies that harvest biomechanical energy from human motion such as foot strike, joint motion, and upper limb motion. They are classified based on the typical principle of kinetic energy harvesting: piezoelectric, triboelectric, and electromagnetic energy harvesting. We focused on the wearing position of high-power wearable biomechanical energy harvesters (WBEHs) generating watt-level power. In addition, the features and future trends of the watt-level WBEHs are discussed.

This paper explores the potential of micro-pin-fin heat sinks as an effective alternative to microchannel heat sinks for dissipating high heat fluxes from small areas. The overall goal is to compare microchannel and micro-pin-fin heat sinks based on three metrics: thermal performance, hydraulic performance, and cost of manufacturing. The channels and pins of the microchannel and micro-pin-fin heat sinks, respectively, have a width of 200 ¿m and a height of 670 ¿m. A comparison of the thermal-hydraulic performance shows that the micro-pin-fin heat sink has a lower convection thermal resistance at liquid flow rates above approximately 60 g/min, though this is accompanied by a higher pressure drop. Methods that could feasibly fabricate the two heat sinks are reviewed, with references outlining current capabilities and limitations. A case study on micro-end-milling of the heat sinks is included. This paper includes equations that separate the fabrication cost into the independent variables that contribute to material cost, machining cost, and machining time. It is concluded that, with micro-end-milling, the machining time is the primary factor in determining cost, and, due to the additional machining time required, the micro-pin-fin heat sinks are roughly three times as expensive to make. It is also noted that improvements in the fabrication process, including spindle speed and tool coatings, will decrease the difference in cost.

Aims: In this study, we aimed to explore surgical variations in the Femoral Neck System (FNS) used for stable fixation of Pauwels type III femoral neck fractures. Methods: Finite element models were established with surgical variations in the distance between the implant tip and subchondral bone, the gap between the plate and lateral femoral cortex, and inferior implant positioning. The models were subjected to physiological load. Results: Under a load of single-leg stance, Pauwels type III femoral neck fractures fixed with 10 mm shorter bolts revealed a 7% increase of the interfragmentary gap. The interfragmentary sliding, compressive, and shear stress remained similar to models with bolt tips positioned close to the subchondral bone. Inferior positioning of FNS provided a similar interfragmentary distance, but with 6% increase of the interfragmentary sliding distance compared to central positioning of bolts. Inferior positioning resulted in a one-third increase in interfragmentary compressive and shear stress. A 5 mm gap placed between the diaphysis and plate provided stability comparable to standard fixation, with a 7% decrease of interfragmentary gap and sliding distance, but similar compressive and shear stress. Conclusion: Finite element analysis with FNS on Pauwels type III femoral neck fractures revealed that placement of the bolt tip close to subchondral bone provides increased stability. Inferior positioning of FNS bolt increased interfragmentary sliding distance, compressive, and shear stress. The comparable stability of the fixation model with the standard model suggests that a 5 mm gap placed between the plate and diaphysis could viably adjust the depth of the bolt. Cite this article: Bone Joint Res 2022;11(2):102–111.

Artificial intelligence technology is rapidly developing with the improvement of computer performance and the development of various algorithms, and research using artificial intelligence technology is being actively conducted in the field of manufacturing technology. In the field of welding, research on arc welding quality prediction using artificial neural network algorithms (ANN) was mainly conducted in the early stages. Since then, in the case of arc welding quality prediction using a deep neural network (DNN) algorithm, research has been conducted to increase the accuracy by increasing the hidden layer in the ANN algorithm. Recently, many studies have been conducted in the form of predicting and classifying the quality of arc welding based on the convolutional neural network (CNN) algorithm, which is one of the deep learning algorithms. Therefore, in this paper, we review representative algorithms such as ANN, DNN, and CNN applied to the welding field, and introduce studies that have successfully performed bead width prediction, welding quality prediction, and quality classification.