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Every year, the Republic of Korea experiences numerous landslides, resulting in property damage and casualties. This study compared the abilities of frequency ratio (FR), analytic hierarchy process (AHP), logistic regression (LR), and artificial neural network (ANN) models to produce landslide susceptibility index (LSI) maps for use in predicting possible landslide occurrence and limiting damage. The areas under the relative operating characteristic (ROC) curves for the FR, AHP, LR, and ANN LSI maps were 0.794, 0.789, 0.794, and 0.806, respectively. Thus, the LSI maps developed by all the models had similar accuracy. A cross-tabulation analysis of landslide occurrence against non-occurrence areas showed generally similar overall accuracies of 65.27, 64.35, 65.51, and 68.47 % for the FR, AHP, LR, and ANN models, respectively. A correlation analysis between the models demonstrated that the LR and ANN models had the highest correlation (0.829), whereas the FR and AHP models had the lowest correlation (0.619).

Lattice oxygen redox offers an unexplored way to access superior electrochemical properties of transition metal oxides (TMOs) for rechargeable batteries. However, the reaction is often accompanied by unfavourable structural transformations and persistent electrochemical degradation, thereby precluding the practical application of this strategy. Here we explore the close interplay between the local structural change and oxygen electrochemistry during short- and long-term battery operation for layered TMOs. The substantially distinct evolution of the oxygen-redox activity and reversibility are demonstrated to stem from the different cation-migration mechanisms during the dynamic de/intercalation process. We show that the π stabilization on the oxygen oxidation initially aids in the reversibility of the oxygen redox and is predominant in the absence of cation migrations; however, the π-interacting oxygen is gradually replaced by σ-interacting oxygen that triggers the formation of O–O dimers and structural destabilization as cycling progresses. More importantly, it is revealed that the distinct cation-migration paths available in the layered TMOs govern the conversion kinetics from π to σ interactions. These findings constitute a step forward in unravelling the correlation between the local structural evolution and the reversibility of oxygen electrochemistry and provide guidance for further development of oxygen-redox layered electrode materials. Transition metal oxide electrodes are promising for rechargeable batteries but are subject to suffer from structural transformations and electrochemical degradation. The evolution of oxygen-redox activity and reversibility in layered electrodes are shown to arise from cation-migration mechanisms during de/intercalation.

Following the unconventional gas revolution, the forecasting of natural gas prices has become increasingly important because the association of these prices with those of crude oil has weakened. With this as motivation, we propose some modified hybrid models in which various combinations of the wavelet approximation, detail components, autoregressive integrated moving average, generalized autoregressive conditional heteroskedasticity, and artificial neural network models are employed to predict natural gas prices. We also emphasize the boundary problem in wavelet decomposition, and compare results that consider the boundary problem case with those that do not. The empirical results show that our suggested approach can handle the boundary problem, such that it facilitates the extraction of the appropriate forecasting results. The performance of the wavelet-hybrid approach was superior in all cases, whereas the application of detail components in the forecasting was only able to yield a small improvement in forecasting performance. Therefore, forecasting with only an approximation component would be acceptable, in consideration of forecasting efficiency.

This study analyzed and compared landslide susceptibility models using decision tree (DT), random forest (RF), and rotation forest (RoF) algorithms at Woomyeon Mountain, South Korea. Out of a total of 145 landslide locations, 102 locations (70%) were used for model training, and the remaining 43 locations (30%) were used for validation. Fourteen landslide conditioning factors were identified, and the contributions of each factor were evaluated using the RRelief-F algorithm with a 10-fold cross-validation approach. Three factors, timber diameter, age, and density had no contribution to landslide occurrence. Landslide susceptibility maps (LSMs) were produced using DT, RF, and RoF models with the 11 remaining landslide conditioning factors: altitude, slope, aspect, profile curvature, plan curvature, topographic position index, elevation-relief ratio, slope length and slope steepness, topographic wetness index, stream power index, and timber type. The performances of the LSMs were assessed and compared based on sensitivity, specificity, precision, accuracy, kappa index, and receiver operating characteristic curves. The results showed that the ensemble learning methods outperformed the single classifier (DT) and that the RoF model had the highest prediction capability compared to the DT and RF models. The results of this study may be helpful in managing areas vulnerable to landslides and establishing mitigation strategies.

As we age, humans see natural decreases in muscle force and power which leads to a slower, less efficient gait. Improving mobility for both healthy individuals and those with muscle impairments/weakness has been a goal for exoskeleton designers for decades. In this work, we discover that significant reductions in the energy cost required for walking can be achieved with almost 50% less mechanical power compared to the state of the art. This was achieved by leveraging human-in-the-loop optimization to understand the importance of individualized assistance for hip flexion, a relatively unexplored joint motion. Specifically, we show that a tethered hip flexion exosuit can reduce the metabolic rate of walking by up to 15.2 ± 2.6%, compared to locomotion with assistance turned off (equivalent to 14.8% reduction compared to not wearing the exosuit). This large metabolic reduction was achieved with surprisingly low assistance magnitudes (average of 89 N, ~ 24% of normal hip flexion torque). Furthermore, the ratio of metabolic reduction to the positive exosuit power delivered was 1.8 times higher than ratios previously found for hip extension and ankle plantarflexion. These findings motivated the design of a lightweight (2.31 kg) and portable hip flexion assisting exosuit, that demonstrated a 7.2 ± 2.9% metabolic reduction compared to walking without the exosuit. The high ratio of metabolic reduction to exosuit power measured in this study supports previous simulation findings and provides compelling evidence that hip flexion may be an efficient joint motion to target when considering how to create practical and lightweight wearable robots to support improved mobility.

Background Soft exosuits are a recent approach for assisting human locomotion, which apply assistive torques to the wearer through functional apparel. Over the past few years, there has been growing recognition of the importance of control individualization for such gait assistive devices to maximize benefit to the wearer. In this paper, we present an updated version of autonomous multi-joint soft exosuit, including an online parameter tuning method that customizes control parameters for each individual based on positive ankle augmentation power. Methods The soft exosuit is designed to assist with plantarflexion, hip flexion, and hip extension while walking. A mobile actuation system is mounted on a military rucksack, and forces generated by the actuation system are transmitted via Bowden cables to the exosuit. The controller performs an iterative force-based position control of the Bowden cables on a step-by-step basis, delivering multi-articular (plantarflexion and hip flexion) assistance during push-off and hip extension assistance in early stance. To individualize the multi-articular assistance, an online parameter tuning method was developed that customizes two control parameters to maximize the positive augmentation power delivered to the ankle. To investigate the metabolic efficacy of the exosuit with wearer-specific parameters, human subject testing was conducted involving walking on a treadmill at 1.50 m s − 1 carrying a 6.8-kg loaded rucksack. Seven participants underwent the tuning process, and the metabolic cost of loaded walking was measured with and without wearing the exosuit using the individualized control parameters. Results The online parameter tuning method was capable of customizing the control parameters, creating a positive ankle augmentation power map for each individual. The subject-specific control parameters and resultant assistance profile shapes varied across the study participants. The exosuit with the wearer-specific parameters significantly reduced the metabolic cost of load carriage by 14.88 ± 1.09% ( P  = 5 × 10 − 5 ) compared to walking without wearing the device and by 22.03 ± 2.23% ( P  = 2 × 10 − 5 ) compared to walking with the device unpowered. Conclusion The autonomous multi-joint soft exosuit with subject-specific control parameters tuned based on positive ankle augmentation power demonstrated the ability to improve human walking economy. Future studies will further investigate the effect of the augmentation-power-based control parameter tuning on wearer biomechanics and energetics.

With the demand for high-energy-storage devices, the rechargeable metal–oxygen battery has attracted attention recently. Sodium–oxygen batteries have been regarded as the most promising candidates because of their lower-charge overpotential compared with that of lithium–oxygen system. However, conflicting observations with different discharge products have inhibited the understanding of precise reactions in the battery. Here we demonstrate that the competition between the electrochemical and chemical reactions in sodium–oxygen batteries leads to the dissolution and ionization of sodium superoxide, liberating superoxide anion and triggering the formation of sodium peroxide dihydrate (Na 2 O 2 ·2H 2 O). On the formation of Na 2 O 2 ·2H 2 O, the charge overpotential of sodium–oxygen cells significantly increases. This verification addresses the origin of conflicting discharge products and overpotentials observed in sodium–oxygen systems. Our proposed model provides guidelines to help direct the reactions in sodium–oxygen batteries to achieve high efficiency and rechargeability. Sodium-oxygen batteries are promising energy storage devices but the nature of their discharge products remains unresolved. Here, the authors reveal that the dissolution and ionization of sodium superoxide leads to the formation of other phases, which increases the charge overpotential of the cell.

Lithium batteries with solid-state electrolytes are an appealing alternative to state-of-the-art non-aqueous lithium-ion batteries with liquid electrolytes because of safety and energy aspects. However, engineering development at the cell level for lithium batteries with solid-state electrolytes is limited. Here, to advance this aspect and produce high-energy lithium cells, we introduce a cell design based on advanced parametrization of microstructural and architectural parameters of electrode and electrolyte components. To validate the cell design proposed, we assemble and test (applying a stack pressure of 3.74 MPa at 45 °C) 10-layer and 4-layer solid-state lithium pouch cells with a solid polymer electrolyte, resulting in an initial specific energy of 280 Wh kg −1 (corresponding to an energy density of 600 Wh L −1 ) and 310 Wh kg −1 (corresponding to an energy density of 650 Wh L −1 ) respectively. Multiscale design principles and empirical processing techniques are considered for the design of high-energy-density Li-based batteries using polymer electrolytes. Here, the authors demonstrate the effectiveness of this approach by assembling and testing ampere-hour-level solid-state lithium-based pouch cells.

Diarrhea, a common symptom of gastrointestinal infections, can lead to severe complications and is a major cause of emergency department (ED) visits. This study explored the temporal association between internet search queries for diarrhea and its synonyms and ED visits for diarrhea-related symptoms. We used data from the National Emergency Department Information System (NEDIS) and NAVER (Naver Corporation), South Korea's leading search engine, from January 2017 to December 2021. After identifying diarrhea synonyms using ChatGPT, we compared weekly trends in relative search volumes (RSVs) for diarrhea, including its synonyms and weekly ED visits. Pearson correlation analysis and Granger causality tests were used to evaluate the relationship between RSVs and ED visits. We developed an Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) model to further predict these associations. This study also examined the age-based distribution of search behaviors and ED visits. A significant correlation was observed between the weekly RSV for diarrhea and its synonyms and weekly ED visits for diarrhea-related symptoms (ranging from 0.14 to 0.51, P<.05). Weekly RSVs for diarrhea synonyms, such as \"upset stomach,\" \"watery diarrhea,\" and \"acute enteritis,\" showed stronger correlations with weekly ED visits than weekly RSVs for the general term \"diarrhea\" (ranging from 0.20 to 0.41, P<.05). This may be because these synonyms better reflect layperson terminology. Notably, weekly RSV for \"upset stomach\" was significantly correlated with weekly ED visits for diarrhea and acute diarrhea at 1 and 2 weeks before the visit (P<.05). An ARIMAX model was developed to predict weekly ED visits based on weekly RSVs for diarrhea synonyms with lagged effects to capture their temporal influence. The age group of <50 years showed the highest activity in both web-based searches and ED visits for diarrhea-related symptoms. This study demonstrates that weekly RSVs for diarrhea synonyms are associated with weekly ED visits for diarrhea-related symptoms. By encompassing a nationwide scope, this study broadens the existing methodology for syndromic surveillance using ED data and provides valuable insights for clinicians.

To address the urgent demand for sustainable battery manufacturing, this review contrasts traditional wet process with emerging dry electrode technologies. Dry process stands out because of its reduced energy and environmental footprint, offering considerable economic benefits and facilitating the production of high‐energy‐density electrodes. We spotlight technological innovations that exemplify the paradigm shift towards eco‐friendliness and cost‐efficiency. This review synthesizes the latest developments in dry electrode production, comparing the techniques with conventional methods, and outlines future research for further optimization toward a higher technology readiness level. We suggest that the evolution of battery manufacturing hinges on the synergy between process innovation and materials science, which is crucial for meeting the dual goals of environmental sustainability and economic practicality. Developing a process for dry electrode fabrication is required to achieve high‐energy‐density batteries and carbon neutralization through thick electrode construction and organic solvent removal, respectively. This review highlights promising concepts focused on manufacturing processes and binder materials of dry electrode to substitute slurry‐based electrode.