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Progressive global aging has increased the number of elderly individuals living alone. The consequent rise in fall accidents has worsened physical injuries, reduced the quality of life, and increased medical expenses. Existing wearable fall-detection devices may cause discomfort, and camera-based systems raise privacy concerns. Here, we propose a non-contact fall-detection system that integrates 4D imaging radar sensors with artificial intelligence (AI) technology to detect falls through real-time monitoring and visualization using a web-based dashboard and Unity engine-based avatar, along with immediate alerts. The system eliminates the need for uncomfortable wearable devices and mitigates the privacy issues associated with cameras. The radar sensors generate Point Cloud data (the spatial coordinates, velocity, Doppler power, and time), which allow analysis of the body position and movement. A CNN model classifies postures into standing, sitting, and lying, while changes in the speed and position distinguish falling actions from lying-down actions. The Point Cloud data were normalized and organized using zero padding and k-means clustering to improve the learning efficiency. The model achieved 98.66% accuracy in posture classification and 95% in fall detection. This study demonstrates the effectiveness of the proposed fall detection approach and suggests future directions in multi-sensor integration for indoor applications.

Hydrogen stands as a promising energy carrier that plays a pivotal role in addressing global sustainability and achieving carbon neutrality. The conversion of hydrogen energy through fuel cells has emerged as a central technology in this pursuit. Notably, protonic ceramic fuel cells (PCFCs) hold potential for the future hydrogen energy ecosystem, owing to their impressive energy conversion efficiencies at low‐to‐intermediate temperatures (300–750°C). It is becoming increasingly evident that the development of PCFC technology relies on advancements in the cathode, as oxygen‐involved reactions often exhibit sluggish kinetics. In this comprehensive review, we aim to provide an overview of the current state of knowledge concerning the design of advanced cathodes for PCFCs. This includes discussing key descriptors for cathodes, methods for characterizing material properties, and functionalization techniques to enhance electrode performance. Finally, we present insights into future research directions. Hydrogen is a promising energy source, and protonic ceramic fuel cells (PCFCs) offer high efficiency at moderate temperatures. However, improving the cathode is essential due to slow oxygen reaction kinetics. This review discusses advanced cathode designs, characterization methods, and future research directions.

Recently, cybercrimes that exploit the anonymity of blockchain are increasing. They steal blockchain users’ assets, threaten the network’s reliability, and destabilize the blockchain network. Therefore, it is necessary to detect blockchain cybercriminal accounts to protect users’ assets and sustain the blockchain ecosystem. Many studies have been conducted to detect cybercriminal accounts in the blockchain network. They represented blockchain transaction records as homogeneous transaction graphs that have a multi-edge. They also adopted graph learning algorithms to analyze transaction graphs. However, most graph learning algorithms are not efficient in multi-edge graphs, and homogeneous graphs ignore the heterogeneity of the blockchain network. In this paper, we propose a novel heterogeneous graph structure called an account-transaction graph, ATGraph. ATGraph represents a multi-edge as single edges by considering transactions as nodes. It allows graph learning more efficiently by eliminating multi-edges. Moreover, we compare the performance of ATGraph with homogeneous transaction graphs in various graph learning algorithms. The experimental results demonstrate that the detection performance using ATGraph as input outperforms that using homogeneous graphs as the input by up to 0.2 AUROC.

Highlights Laser-induced percolation interlocking technology enables the development of the robust, open-structured nanowire (NW) electrodes through the photothermal effect at the interface between NW and substrate. The optimized NW electrode with enhanced mechanical and electrical properties can be used as reusable wearable electronics. Stable functionalization of the percolation-interlocked NW electrode with various conducting polymers can be achieved, broadening the applicability as soft electronics. Metallic nanowires have served as novel materials for soft electronics due to their outstanding mechanical compliance and electrical properties. However, weak adhesion and low mechanical robustness of nanowire networks to substrates significantly undermine their reliability, necessitating the use of an insulating protective layer, which greatly limits their utility. Herein, we present a versatile and generalized laser-based process that simultaneously achieves strong adhesion and mechanical robustness of nanowire networks on diverse substrates without the need for a protective layer. In this method, the laser-induced photothermal energy at the interface between the nanowire network and the substrate facilitates the interpenetration of the nanowire network and the polymer matrix, resulting in mechanical interlocking through percolation. This mechanism is broadly applicable across different metallic nanowires and thermoplastic substrates, significantly enhancing its universality in diverse applications. Thereby, we demonstrated the mechanical robustness of nanowires in reusable wearable physiological sensors on the skin without compromising the performance of the sensor. Furthermore, enhanced robustness and electrical conductivity by the laser-induced interlocking enables a stable functionalization of conducting polymers in a wet environment, broadening its application into various electrochemical devices.

Hafnium oxide (HfOx) films have a wide range of applications in solid-state devices, including metal–oxide–semiconductor field-effect transistors (MOSFETs). The growth of HfOx films from the metal precursor tetrakis(ethylmethylamino) hafnium with La(NO3)3·6H2O solution (LNS) as an oxidant was investigated. The atomic layer deposition (ALD) conditions were optimized, and the chemical state, surface morphology, and microstructure of the prepared films were characterized. Furthermore, to better understand the effects of LNS on the deposition process, HfOx films deposited using a conventional oxidant (H2O) were also prepared. The ALD process using LNS was observed to be self-limiting, with an ALD temperature window of 200–350 °C and a growth rate of 1.6 Å per cycle, two times faster than that with H2O. HfOx films deposited using the LNS oxidant had smaller crystallites than those deposited using H2O, as well as more suboxides or defects because of the higher number of grain boundaries. In addition, there was a difference in the preferred orientations of the HfOx films deposited using LNS and H2O, and consequently, a difference in surface energy. Finally, a film growth model based on the surface energy difference was proposed to explain the observed growth rate and crystallite size trends.