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Apios is a legume known to be nutrient dense. However, it is difficult for older adults to consume due to chewing difficulties. In this study, we analyzed its nutritional contents and antioxidant capacity and improved its texture through enzymatic treatment (polygalacturonase, cellulase, and α-amylase) to make it more palatable for older adults. Apios has been shown to have higher antioxidant capacity than other tubers, which is beneficial for geriatric health. Enzyme treatment (ETG1 7.12 h, ETG2 9.64 h, ETG3 over 6.48 h) made the texture suitable for consumption by the elderly. SEM analysis showed that the pore size increased and the cell wall was disrupted, resulting in irregular cell tissues. These changes can facilitate mastication and improve nutrient intake. Therefore, the enzyme-treated Apios is easy to chew, so it will be able to improve the intake of nutrients that the elderly need. In addition, it is expected to contribute to the expansion of the elder-friendly food industry by applying enzyme treatment to personalized food development considering different chewing abilities of the elderly.

Abstract Dynamic random-access memory (DRAM) has been scaled down to meet high-density, high-speed, and low-power memory requirements. However, conventional DRAM has limitations in achieving memory reliability, especially sufficient capacitance to distinguish memory states. While there have been attempts to enhance capacitor technology, these solutions increase manufacturing cost and complexity. Additionally, Silicon-based capacitorless memories have been reported, but they still suffer from serious difficulties regarding reliability and power consumption. Here, we propose a novel Schottky barrier memory (SBRAM), which is free of the complex capacitor structure and features a heterojunction based on bandgap engineering. SBRAM can be configured as vertical cross-point arrays, which enables high-density integration with a 4F2 footprint. In particular, the Schottky junction significantly reduces the reverse leakage current, preventing sneak current paths that cause leakage currents and readout errors during array operation. Moreover, the heterojunction physically divides the storage region into two regions, resulting in three distinct resistive states and inducing a gradual current slope to ensure sufficient holding margin. These states are determined by the holding voltage (V hold) applied to the programmed device. When the V hold is 1.1 V, the programmed state can be maintained with an exceptionally low current of 35.7 fA without a refresh operation.

Abstract We have proposed leaky integrate-and-fire (LIF) neuron having low-energy consumption and tunable functionality without external circuit components. Our LIF neuron has a simple configuration consisting of only three components: one bandgap-engineered resistive switching transistor (BE-RST), one capacitor, and one resistor. Here, the crucial point is that BE-RST with a silicon–germanium heterojunction possesses an amplified hysteric current switching with a low latch-up voltage due to improved hole storage capability and impact ionization coefficient. Therefore, the proposed neuron utilizing BE-RST requires an energy consumption of 0.36 pJ/spike, which is approximately six times lower than 2.08 pJ/spike of pure silicon-RST based neuron. In addition, the spiking properties can be tuned by modulating the leakage rate and threshold through gate bias, which contributes to energy-efficient sparse-activity and high learning accuracy. As a result, our proposed neuron can be a promising candidate for executing various spiking neural network applications.

Although the hemostatically efficacious nature of cellulose nanofibers (CNFs) is well established, data regarding their hemostatic performance in large animal systems and safety toward human skin cells, which are critical factors for clinical translation, are lacking. This study evaluated the clinical potential of CNF-coated gauze by testing its hemostatic efficacy in rats, rabbits, and pigs, and assessing its safety in human primary cells through differential gene expression (DEG) analysis. In-vitro studies using rabbit blood revealed that the CNF-coated gauze exhibited a significantly lower blood clotting index (BCI) value than gauze coated with chitosan, a commonly used hemostatic agent, indicating superior blood clotting. In-vivo tests on bleeding-induced pig livers revealed that the CNF-coated gauze delivered a four-fold higher hemostatic success rate reduced bleeding volume compared to regular gauze. Moreover, a rat wound-healing model revealed improved healing with CNF-coated gauze. DEG analysis of human dermal fibroblast primary cells showed no statistically significant differences between the CNF and control groups, indicating its human safety. Overall, our results suggest that CNF-coated hemostatic gauze absorbs blood rapidly, delivers superior hemostatic performance compared to regular gauze, is safe from both histological and DEG perspectives, and is therefore suitable for use in healthcare and clinical settings.

Abstract We demonstrate a highly biomimetic spiking neuron capable of fast and energy-efficient neuronal oscillation dynamics. Our simple neuron circuit is constructed using silicon–germanium heterojunction based bipolar transistors (HBTs) with nanowire structure. The HBT has a hysteresis window with steep switching characteristics and high current margin in the low voltage range, which enables a high spiking frequency (~ 245 kHz) with low energy consumption (≤ 1.37 pJ/spike). Also, gated structure achieves a stable balance in the activity of the neural system by incorporating both excitatory and inhibitory signal. Furthermore, inhibition of multiple strengths can be realized by adjusting the integration time according to the amplitude of the inhibitory signal. In addition, the spiking frequency can be tuned by mutually controlling the hysteresis window in the HBTs. These results ensure the sparse activity and homeostasis of neural networks.

This paper presents a physical space design method for mixed reality-based immersive environments by improving the color rendering of optically visible head-mounted displays. In OST-HMD, the phenomenon that the light of the external environment is combined with the enhanced image occurs, which reduces the visibility of the content. Moreover, previous studies to solve this problem required additional optical components or performed only simple verification. Proposed physical space was designed using black curtains, blinds, low-reflection structures, and adjustable lighting. The physical space can be adapted to the intended use of the physical space through physical transitions for multiple users without additional optics. To verify the usefulness of this environment, we measured the color gamut and intensity spectrum in three experimental environments. Also, we analyzed the factors that change the color gamut according to the components and the surrounding environment. As a result, the color gamut improved by up to 4.7 times from 19.92% to 93.53% based on the environment and improved by up to 4.1 times from 21% to 86.29% depending on the location. The experimental results revealed that the presented method could reduce the factors that degrade the color gamut within all measurement locations and significantly improve the color gamut.

Coccinia grandis (C. grandis) is a perennial plant belonging to the Cucurbitaceae family. It is primarily native to Southeast Asia but has recently been cultivated domestically in Korea due to climate change. In this study, immature fruits of C. grandis, which are rich in bioactive substances, were processed into acidified pickles and stored for 15–55 days to investigate changes in their phytochemicals. The individual components of the phytochemicals in C. grandis were identified using an ultra-high-performance liquid chromatography system, and the main substances that were identified in this step then quantified using a triple quadrupole mass spectrometer. The qualitative analysis tentatively identified a total of 30 compounds (fourteen hydroxycinnamic acids, one hydroxybenzoic acids, thirteen flavonols and two triterpenes) in C. grandis pickles. Quantification of the phytochemicals showed that the major polyphenols—which included ferulic acid, p-coumaric acid, rutin and kaempferol—were highest on day 25 of aging and gradually decreased thereafter. Moreover, triterpenes decreased rapidly as the fruit was processed into pickles, and they further decreased with increasing time in aging. These results therefore indicate that C. grandis fruit has various bioactive substances that make it suitable for use in functional food development through pickle production.

Intelligent robot arms are advanced robotic systems used in Industry 4.0 to perform complex tasks. Unlike conventional robot arms, which perform predefined tasks, intelligent robot arms have autonomy and can operate in changing environments, interact with other machines, and collaborate with humans. In this regard, adaptive path planning is crucial for intelligent robot arms, involving real-time environment monitoring and path generation to continuously update the robot’s trajectory based on changes in the surroundings. This paper presents an adaptive path planning method for intelligent robot arms to be used in dynamic environments. The proposed method is based on a hybrid active-passive approach and has been tested in a dynamic workspace simulation environment. The results indicate the ability of the proposed method to respond dynamically in a complex scenario where the target is fluctuating, and an obstacle is intentionally placed in the robot’s path. Additionally, real-time analysis results show that the method can be categorized as real-time path planning with less than 100 ms reaction time for grid sizes with less than $96\times 96 \times 96$ cells. This insight presents opportunities for the establishment of smart factories, smart homes, and smart cities, where the presence of intelligent robot arms in dynamic environments becomes essential.

Computer vision-based path planning can play a crucial role in numerous technologically driven smart applications. Although various path planning methods have been proposed, limitations, such as unreliable three-dimensional (3D) localization of objects in a workspace, time-consuming computational processes, and limited two-dimensional workspaces, remain. Studies to address these problems have achieved some success, but many of these problems persist. Therefore, in this study, which is an extension of our previous paper, a novel path planning approach that combined computer vision, Q-learning, and neural networks was developed to overcome these limitations. The proposed computer vision-neural network algorithm was fed by two images from two views to obtain accurate spatial coordinates of objects in real time. Next, Q-learning was used to determine a sequence of simple actions: up, down, left, right, backward, and forward, from the start point to the target point in a 3D workspace. Finally, a trained neural network was used to determine a sequence of joint angles according to the identified actions. Simulation and experimental test results revealed that the proposed combination of 3D object detection, an agent-environment interaction in the Q-learning phase, and simple joint angle computation by trained neural networks considerably alleviated the limitations of previous studies.

Path planning for robot arms to reach a target and avoid obstacles has had a crucial role in manufacturing automation. Although many path planning algorithms, including RRT, APF, PRM, and RL-based, have been presented, they have many problems: a time-consuming process, high computational costs, slowness, non-optimal paths, irregular paths, failure to find a path, and complexity. Scholars have tried to address some of these issues. However, those methods still suffer from slowness and complexity. In order to address these two limitations, this paper presents a new hybrid path planning method that contains two separate parts: action-finding (active approach) and angle-finding (passive approach). In the active phase, the Q-learning algorithm is used to find a sequence of simple actions, including up, down, left, and right, to reach the target cell in a gridded workspace. In the passive phase, the joints angles of the robot arm, with respect to the found actions, are obtained by the trained neural network. The simulation and test results show that this hybrid approach significantly improves the slowness and complexity due to using the simplified agent-environment interaction in the active phase and simple computing the joints angles in the passive phase.

We report a case of Valsalva retinopathy associated with esophagogastroduodenoscopy (EGD) under propofol sedation. A 43-year-old woman who had no previous history of systemic or ocular disease presented with a complaint of decreased vision in her left eye, which developed one day after EGD under propofol sedation. According to the referring physician, the patient had experienced multiple sustained Valsalva maneuvers during EGD. The fundus examination of the left eye showed a large preretinal hemorrhage surrounded by multiple small retinal hemorrhages in the posterior pole. One month later, fundus examination revealed a floating organized vitreous hemorrhage. The pars plana vitrectomy was performed to treat persistent vitreous hemorrhage. One month after vitrectomy, fundus examination showed normal retina and the patient's vision recovered to 20/20. Valsalva maneuver can occur during EGD under sedation, and Valsalva retinopathy should be considered as a possible cause. Valsalva retinopathy should be included in the differential diagnosis when a patient complains of blurred vision following EGD.