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The fuel cell industry is the most promising industry in terms of the advancement of clean and safe technologies for sustainable energy generation. The polymer electrolyte membrane fuel cell is divided into two parts: anion exchange membrane fuel cells (AEMFCs) and proton exchange membrane fuel cells (PEMFCs). In the case of PEMFCs, high-power density was secured and research and development for commercialization have made significant progress. However, there are technical limitations and high-cost issues for the use of precious metal catalysts including Pt, the durability of catalysts, bipolar plates, and membranes, and the use of hydrogen to ensure system stability. On the contrary, AEMFCs have been used as low-platinum or non-platinum catalysts and have a low activation energy of oxygen reduction reaction, so many studies have been conducted to find alternatives to overcome the problems of PEMFCs in the last decade. At the core of ensuring the power density of AEMFCs is the anion exchange membrane (AEM) which is less durable and less conductive than the cation exchange membrane. AEMFCs are a promising technology that can solve the high-cost problem of PEMFCs that have reached technological saturation and overcome technical limitations. This review focuses on the various aspects of AEMs for AEMFCs application.

Cellulose and its derivatives have evoked much attention in sensor technology as host-matrices for conducting materials because of their versatility, renewability, and biocompatibility. However, only a few studies have dealt with the potential utilization of cellulose as a sensing material without a composite structure. In this study, cellulose nanofibers (CNF) and 2,2,6,6-tetramethylpiperidine-1-oxyl (TEMPO)-oxidized cellulose nanofibers (TOCNF) extracted from rice husks by using ultrasonic-assisted methods are introduced as a potential gas sensing material with highly sensitive performance. To fabricate nanocellulose-based films, CNF, TOCNF, and TOCNF with glycerol (TOCNF/G) were dispersed in water and applied on polyimide substrate with digital electrodes to form self-standing thin films by a drop-casting method. A transparent coating layer on the surface of the plate after drying is used for the detection of water-soluble gases such as acetone, ammonia, methane, and hydrogen sulfide gases at room temperature at 52% relative humidity. The sensor prototypes exhibited high sensitivity, and the detection limit was between 1 ppm and 5 ppm, with less than 10 min response and recovery time. The results indicate that both the CNF- and the TOCNF-coated sensors show good sensitivity toward ammonia and acetone, compared to other gases. A TOCNF/G-coated sensor exhibited minimum time in regard to response/recovery time, compared to a CNF-coated sensor. In this study, nanocellulose-based sensors were successfully fabricated using a low-cost process and a bio-based platform. They showed good sensitivity for the detection of various gases under ambient conditions. Therefore, our study results should further propel in-depth research regarding various applications of cellulose-based sensors in the future.

Background/Objective: The Chinese government is actively developing the rural economy and promoting the poverty alleviation campaign. The economic development of the traditional agricultural areas is essential to people's basic livelihood and social stability. The tourism industry has been proven to be an effective approach to promote the regional economy. It has become a hot issue as to how to develop the tourism industry in rural areas. Methods/Statistical Analysis: Based on a corresponding index system, a coupling coordination model was established to explore the coupling and coordination development of the agricultural industry and the tourism industry in Henan province, a traditional agricultural area in China. Findings: The result revealed that although the coupling degree between the agricultural and the tourism industry from the year of 2009 to 2018 is relatively stable, the coordination degree shows a continuous rising trend from 0.278 to 0.921. This indicated that the agricultural industry and the tourism industry in Henan province continues to interact with and influence each other, and the comprehensive development level and the coordination degree of the two industries have been constantly improved. Implications: From the result, it can be seen that the integration development of the agricultural industry and the tourism industry could be a new path to develop the economy of rural areas. From the perspective of sustainable development, suggestions are proposed to optimize the agricultural industrial structure, extend tourism industry chain and construct support policy to develop a sustainable mode of the agricultural industry and the tourism industry.

Various robotic arm control systems have been proposed to support the daily lives of patients with severe motor impairments; however, existing robotic arm control devices typically require physical input devices, such as joysticks, which are often difficult for patients with motor impairments to use. To overcome these limitations, this paper proposes a new method for controlling a robotic arm using augmented reality and object detection. The system automatically configures the path from the robotic arm to the object, allowing all operations to be performed using only eye tracking. With precise object localization and an intuitive gaze-based interface, the proposed robotic arm control system offers a significant advantage for patients with motor impairments, providing a more accessible and user-friendly alternative to traditional control methods.

Motor imagery (MI) brain–computer interfaces (BCIs) have been used for a wide variety of applications due to their intuitive matching between the user’s intentions and the performance of tasks. Applying dry electroencephalography (EEG) electrodes to MI BCI applications can resolve many constraints and achieve practicality. In this study, we propose a multi-domain convolutional neural networks (MD-CNN) model that learns subject-specific and electrode-dependent EEG features using a multi-domain structure to improve the classification accuracy of dry electrode MI BCIs. The proposed MD-CNN model is composed of learning layers for three domain representations (time, spatial, and phase). We first evaluated the proposed MD-CNN model using a public dataset to confirm 78.96% classification accuracy for multi-class classification (chance level accuracy: 30%). After that, 10 healthy subjects participated and performed three classes of MI tasks related to lower-limb movement (gait, sitting down, and resting) over two sessions (dry and wet electrodes). Consequently, the proposed MD-CNN model achieved the highest classification accuracy (dry: 58.44%; wet: 58.66%; chance level accuracy: 43.33%) with a three-class classifier and the lowest difference in accuracy between the two electrode types (0.22%, d = 0.0292) compared with the conventional classifiers (FBCSP, EEGNet, ShallowConvNet, and DeepConvNet) that used only a single domain. We expect that the proposed MD-CNN model could be applied for developing robust MI BCI systems with dry electrodes.

This study proposes the development of a wireless sensor system integrated with smart ultra-high performance concrete (UHPC) for sensing and transmitting changes in stress and damage occurrence in real-time. The smart UHPC, which has the self-sensing ability, comprises steel fibers, fine steel slag aggregates (FSSAs), and multiwall carbon nanotubes (MWCNTs) as functional fillers. The proposed wireless sensing system used a low-cost microcontroller unit (MCU) and two-probe resistance sensing circuit to capture change in electrical resistance of self-sensing UHPC due to external stress. For wireless transmission, the developed wireless sensing system used Bluetooth low energy (BLE) beacon for low-power and multi-channel data transmission. For experimental validation of the proposed smart UHPC, two types of specimens for tensile and compression tests were fabricated. In the laboratory test, using a universal testing machine, the change in electrical resistivity was measured and compared with a reference DC resistance meter. The proposed wireless sensing system showed decreased electrical resistance under compressive and tensile load. The fractional change in resistivity (FCR) was monitored at 39.2% under the maximum compressive stress and 12.35% per crack under the maximum compressive stress tension. The electrical resistance changes in both compression and tension showed similar behavior, measured by a DC meter and validated the developed integration of wireless sensing system and smart UHPC.

The rapidly changing climate is impacting species globally at an unprecedented rate, including humans. Consequently, extensive research is being conducted on the impacts of climate change on indigenous and vulnerable species. However, landscape trees, which are cultivated and managed by humans, receive less attention despite their significant role in urban environments. Landscape tree also have specific climatic ranges and environmental requirements, making them susceptible to climate change. In this study, we predicted the future sustainability of three native landscape trees ( Stewartia koreana , Betula ermanii , and Taxus cuspidata ) using maximum entropy (MaxEnt) models under SSP2-4.5 and SSP5-8.5 climate scenarios. A time-series analysis of suitability was conducted, and the resulting maps were overlaid to classify regions of suitability. The findings indicate a general northward shift in climate suitability and a potential reduction in long-term suitable areas for all three species. Under the SSP2-4.5 scenario, potential suitable area for S. koreana increased, while those for B. ermanii , T. cuspidata decreased by the 2090s. Under the SSP5-8.5 scenario, suitable areas for S. koreana , B. ermanii , T. cuspidata decreased by 33.6%, 98.9%, and 90.1%, respectively. The climate suitability classification (“Sustainable suitability”, “Risk”, “Inflow”, “Lost”, and “Variable” regions) effectively identified areas of sustainability and risk, as well as regions requiring management. A notable decline in “Sustainable suitability” regions, which remained suitable from the present to the 2090s, was observed under the SSP5-8.5 scenario relative to SSP2-4.5. The methods utilized in this study offer valuable insights for future landscape planning and conservation. This research underscores the need for adaptive strategies to mitigate potential economic and ecological impacts of climate change by utilizing species distribution models for sustainable landscape planning and tree conservation.

Electroencephalography (EEG) plays a vital role in diagnosing and managing epilepsy, but the traditional technique requires numerous electrodes, making it time-consuming and burdensome for patients. To overcome this, several studies propose reducing the number of channels, often requiring patient-specific selections, which limits their generalizability. This study presents a novel network-based approach for channel selection during the preictal period. We employed 1078 preictal EEG signals collected from 33 epilepsy patients at Korea University Anam Hospital. Using these signals, we established a functional connectivity network and used Pearson’s correlation coefficient to assess the correlation between the selected channels’ network features and the full network’s features. Our findings revealed that a network composed of eight channels, four in each hemisphere located in the midline frontal and parietal regions, exhibited a high correlation with the entire network. Encouragingly, applying the method to the publicly available Siena database yielded similar results. These findings indicate that selecting channels in the midline frontal and parietal regions during the preictal phase effectively preserves the entire network’s features. This suggests that our proposed channel reduction approach could not only enhance our understanding of various brain network-related diseases but also improve patient comfort and efficiency in clinical settings.

Neurogenic claudication is a typical manifestation of lumbar spinal stenosis (LSS). However, its pathophysiology is still unclear. The severity of clinical symptoms has been shown not to correlate with the degree of structural stenosis. Altered cerebrospinal fluid (CSF) flow has been suggested as one of the causative factors of LSS. The objectives of this study were to compare CSF dynamics at the lumbosacral level between patients with LSS and healthy controls and to investigate whether CSF dynamics parameters explain symptom severity in LSS. Phase-contrast magnetic resonance imaging (PC-MRI) was conducted to measure CSF dynamics in 18 healthy controls and 9 patients with LSS. Cephalic peak, caudal peak, and peak-to-peak CSF velocities were evaluated at the lumbosacral level in the patients and controls. The power of CSF dynamics parameters to predict symptom severity was determined using a linear regression analysis adjusted for demographic and structural variables. Significantly attenuated CSF flow velocity was observed in the patients compared with the controls. The cephalic peak, caudal peak, and peak-to-peak velocities at the lumbar level were greater in the controls than in the patients (p<0.001). The predictive power increased most when the peak-to-peak velocity was added (adjusted R 2 = 0.410) to the model with age, body mass index, and the minimum anterior-posterior diameter (adjusted R 2 = 0.306), and the peak-to-peak velocity was the only statistically significant variable. CSF dynamics variables showed an association with the severity of LSS symptoms, independent of structural stenosis. PC-MRI can help to further our understanding of the pathophysiology of neurogenic claudication and support the diagnosis of LSS.

The molten salt reactor is a fourth-generation nuclear power plant considered a long-term eco-friendly energy source with high efficiency and the potential for green hydrogen production. The selection of alloys for such reactors, which can operate for more than 30 years, is a primary concern because of corrosion by high-temperature molten salt. In this study, three Fe- and Ni-based alloys were selected as structural material candidates. Corrosion immersion tests were conducted in NaCl–KCl molten salt for 48 h at 800 °C and 40% RH conditions in an air environment. In the absence of moisture and oxygen removal, ClNaK salt-induced damage was observed in the investigated alloys. The corrosion behavior of the alloys was characterized using various techniques, including scanning electron microscopy, energy-dispersive X-ray spectroscopy, X-ray photoelectron spectroscopy, and Auger electron spectroscopy. The results show that the corrosion process can be explained by salt-induced surface damage, internal ion migration, and depletion to the surface. The corrosion rate is high in SS316L (16Cr-Fe), N10003 (7Cr-Ni), and C-276 (16Cr-Ni), in decreasing order. Based on the corrosion penetration, ion elution, and interfacial diffusion results, C-276 and N10003 are good candidates for structural materials for MSRs. Therefore, Ni-based alloys with high Cr content minimize surface damage and ion depletion in unpurified molten salt environments. This indicates that Ni-based alloys with high Cr content exhibit highly corrosion resistance.