Explore the vast range of titles available.

Fatal casualties resulting from explosions of electric vehicles and energy storage systems equipped with lithium-ion batteries have become increasingly common worldwide. As a result, interest in developing safer and more advanced battery systems has grown. Aqueous batteries are emerging as a promising alternative to lithium-ion batteries, which offer advantages such as low cost, safety, high ionic conductivity, and environmental friendliness. In this Review, we discuss the challenges and recent strategies for various aqueous battery systems that use lithium, zinc, sodium, magnesium, and aluminium ions as carrier ions. We also highlight the three key factors that need the most improvement in these aqueous battery systems: higher operating voltage for the cathode, a more stable metal anode interface, and a larger electrochemical stability window of the electrolyte. Aqueous batteries are emerging as a promising alternative to lithium-ion batteries. In this Review, the challenges and recent strategies for various aqueous battery systems are discussed with key factors needing the most improvement highlighted.

Introduction Epidemic modeling is crucial for understanding and predicting infectious disease spread. To capture the complexity of real-world transmission, dynamic interactions between individuals with spatial heterogeneity must be considered. This modeling requires high-dimensional epidemic parameters, which can lead to unidentifiability; therefore, integrating various data types for inference is essential to effectively address these challenges. Methods We introduce a novel hybrid framework, Multi-Patch Model Update with Graph Attention Network (MPUGAT), that combines a multi-patch compartmental model with a spatio-temporal deep learning model. MPUGAT employs a GAT (Graph Attention Mechanism) to transform static traffic matrices into dynamic transmission matrices by analyzing patterns in diverse time series data from each city. Results We demonstrate the effectiveness of MPUGAT through its application to COVID-19 data from South Korea. By accurately estimating time-varying transmission rates, MPUGAT outperforms traditional models and aligns with actual policies such as social distancing. Conclusion MPUGAT offers a novel approach for effectively integrating easily accessible, low-dimensional, non-epidemic-related data into epidemic modeling frameworks. Our findings highlight the importance of incorporating dynamic data and utilizing graph attention mechanisms to enhance accuracy of infectious disease modeling and the analysis of policy interventions. This study underscores the potential of leveraging diverse data sources and advanced deep learning techniques to improve epidemic forecasting and inform public health strategies.

Consciousness can be defined by two components: arousal (wakefulness) and awareness (subjective experience). However, neurophysiological consciousness metrics able to disentangle between these components have not been reported. Here, we propose an explainable consciousness indicator (ECI) using deep learning to disentangle the components of consciousness. We employ electroencephalographic (EEG) responses to transcranial magnetic stimulation under various conditions, including sleep ( n  = 6), general anesthesia ( n  = 16), and severe brain injury ( n  = 34). We also test our framework using resting-state EEG under general anesthesia ( n  = 15) and severe brain injury ( n  = 34). ECI simultaneously quantifies arousal and awareness under physiological, pharmacological, and pathological conditions. Particularly, ketamine-induced anesthesia and rapid eye movement sleep with low arousal and high awareness are clearly distinguished from other states. In addition, parietal regions appear most relevant for quantifying arousal and awareness. This indicator provides insights into the neural correlates of altered states of consciousness. The authors propose an explainable consciousness indicator using deep learning to quantify arousal and awareness under sleep, anesthesia, and in patients with disorders of consciousness.

Background To compare correlation, agreement, and residual patterns between Patient-Reported Outcomes Measurement Information System (PROMIS) Global Health (GH) and EQ-5D-3 L and to identify areas of convergence and divergence across health domains in a nationally representative Korean population. Methods A total of 2,699 adults completed PROMIS GH and EQ-5D-3 L between December 2021 and January 2022. A linear regression model was used to predict EQ-5D-3 L index values from PROMIS physical and mental health T-score. Agreement between predicted and observed EQ-5D values was assessed using intraclass correlation coefficients (ICC) and root mean squared error (RMSE). Residuals between observed and predicted EQ-5D values were examined to detect systematic discrepancies across demographic, clinical subgroups, and EQ-5D domain severity levels. Results The relationship between PROMIS GH and EQ-5D showed consistent agreement (RMSE = 0.079). Approximately 9.8% of participants showed large discrepancies (absolute residual > 0.1) between observed and PROMIS-predicted EQ-5D values. Divergence was most pronounced in those with mobility, self-care, or usual activities problems, and poor PROMIS Physical Health was the strongest predictor of large discrepancies (Odds ratio = 3.68). Conclusions PROMIS GH and EQ-5D-3 L showed overall agreement but also systematic discrepancies across subgroups. Larger differences were observed in individuals with functional limitations, particularly in mobility, self-care, and usual activities domains. These patterns suggest that the two instruments may capture related but not identical aspects of health.

Sleep is important to maintain physical and cognitive functions in everyday life. However, the prevalence of sleep disorders is on the rise. One existing solution to this problem is to induce sleep using an auditory stimulus. When we listen to acoustic beats of two tones in each ear simultaneously, a binaural beat is generated which induces brain signals at a specific desired frequency. However, this auditory stimulus is uncomfortable for users to listen to induce sleep. To overcome this difficulty, we can exploit the feelings of calmness and relaxation that are induced by the perceptual phenomenon of autonomous sensory meridian response (ASMR). In this study, we proposed a novel auditory stimulus for inducing sleep. Specifically, we used a 6 Hz binaural beat corresponding to the center of the theta band (4-8 Hz), which is the frequency at which brain activity is entrained during non-rapid eye movement (NREM) in sleep stage 1. In addition, the \"ASMR triggers\" that cause ASMR were presented from natural sound as the sensory stimuli. In session 1, we combined two auditory stimuli (the 6 Hz binaural beat and ASMR triggers) at three-decibel ratios to find the optimal combination ratio. As a result, we determined that the combination of a 30:60 dB ratio of binaural beat to ASMR trigger is most effective for inducing theta power and psychological stability. In session 2, the effects of these combined stimuli (CS) were compared with an only binaural beat, only the ASMR trigger, or a sham condition. The combination stimulus retained the advantages of the binaural beat and resolved its shortcomings with the ASMR triggers, including psychological self-reports. Our findings indicate that the proposed auditory stimulus could induce the brain signals required for sleep, while simultaneously keeping the user in a psychologically comfortable state. This technology provides an important opportunity to develop a novel method for increasing the quality of sleep.

Machine learning approaches, XGBoost (XGB) and Random Forest (RF), were applied to estimate methane (CH₄) emissions from paddy fields in Gimje, South Korea, using three years of chamber-based observations. Both models achieved Nash–Sutcliffe efficiency (NSE) values above 0.5, indicating acceptatble predictive performance. XGB better captured extreme values and temporal variability, whereas RF reproduced mean emission trends more reliably, highlighting complementary strengths. Regional simulations produced mean emission factors of 2.64 and 2.35 kg CH₄ ha⁻1 d⁻1 for XGB and RF, respectively, comparable to or slightly higher than the Tier 2 country-specific factor (2.32 kg CH₄ ha⁻1 d⁻1) currently used in Korea’s national greenhouse gas inventory. Spatially, XGB identified high-emission hotspots with greater sensitivity, while RF yielded smoother patterns with lower uncertainty. Model-based regional estimates (6.68–7.51 Gg CH₄ yr⁻1) showed uncertainty ranges of ± 12–13%, substantially lower than the ± 50% default suggested by IPCC guidelines, underscoring the models’ robustness. These results demonstrate the feasibility of applying machine learning to CH₄ estimation at regional scales, thereby addressing the limitations of uniform Tier 2 emission factors. Incorporating region-specific, data-driven approaches can improve inventory accuracy and provide a stronger scientific basis for mitigation strategies. This study highlights the potential of machine learning models to complement existing methods and contribute to the advancement toward a Tier 3 inventory framework for agricultural greenhouse gas reporting.

Motor imagery-based brain-computer interfaces (MI-BCIs) send commands to a computer using the brain activity registered when a subject imagines—but does not perform—a given movement. However, inconsistent MI-BCI performance occurs in variations of brain signals across subjects and experiments; this is considered to be a significant problem in practical BCI. Moreover, some subjects exhibit a phenomenon referred to as “BCI-inefficiency,” in which they are unable to generate brain signals for BCI control. These subjects have significant difficulties in using BCI. The primary goal of this study is to identify the connections of the resting-state network that affect MI performance and predict MI performance using these connections. We used a public database of MI, which includes the results of psychological questionnaires and pre-experimental resting-state taken over two sessions on different days. A dynamic causal model was used to calculate the coupling strengths between brain regions with directionality. Specifically, we investigated the motor network in resting-state, including the dorsolateral prefrontal cortex, which performs motor planning. As a result, we observed a significant difference in the connectivity strength from the supplementary motor area to the right dorsolateral prefrontal cortex between the low- and high-MI performance groups. This coupling, measured in the resting-state, is significantly stronger in the high-MI performance group than the low-MI performance group. The connection strength is positively correlated with MI-BCI performance (Session 1: r = 0.54; Session 2: r = 0.42). We also predicted MI performance using linear regression based on this connection (r-squared = 0.31). The proposed predictors, based on dynamic causal modeling, can develop new strategies for improving BCI performance. These findings can further our understanding of BCI-inefficiency and help BCI users to lower costs and save time.

Cardiac arrest (CA) is the leading cause of death in critically ill patients. Clinical research has shown that early identification of CA reduces mortality. Algorithms capable of predicting CA with high sensitivity have been developed using multivariate time series data. However, these algorithms suffer from a high rate of false alarms, and their results are not clinically interpretable. We propose an ensemble approach using multiresolution statistical features and cosine similarity-based features for the timely prediction of CA. Furthermore, this approach provides clinically interpretable results that can be adopted by clinicians. Patients were retrospectively analyzed using data from the Medical Information Mart for Intensive Care-IV database and the eICU Collaborative Research Database. Based on the multivariate vital signs of a 24-hour time window for adults diagnosed with heart failure, we extracted multiresolution statistical and cosine similarity-based features. These features were used to construct and develop gradient boosting decision trees. Therefore, we adopted cost-sensitive learning as a solution. Then, 10-fold cross-validation was performed to check the consistency of the model performance, and the Shapley additive explanation algorithm was used to capture the overall interpretability of the proposed model. Next, external validation using the eICU Collaborative Research Database was performed to check the generalization ability. The proposed method yielded an overall area under the receiver operating characteristic curve (AUROC) of 0.86 and area under the precision-recall curve (AUPRC) of 0.58. In terms of the timely prediction of CA, the proposed model achieved an AUROC above 0.80 for predicting CA events up to 6 hours in advance. The proposed method simultaneously improved precision and sensitivity to increase the AUPRC, which reduced the number of false alarms while maintaining high sensitivity. This result indicates that the predictive performance of the proposed model is superior to the performances of the models reported in previous studies. Next, we demonstrated the effect of feature importance on the clinical interpretability of the proposed method and inferred the effect between the non-CA and CA groups. Finally, external validation was performed using the eICU Collaborative Research Database, and an AUROC of 0.74 and AUPRC of 0.44 were obtained in a general intensive care unit population. The proposed framework can provide clinicians with more accurate CA prediction results and reduce false alarm rates through internal and external validation. In addition, clinically interpretable prediction results can facilitate clinician understanding. Furthermore, the similarity of vital sign changes can provide insights into temporal pattern changes in CA prediction in patients with heart failure-related diagnoses. Therefore, our system is sufficiently feasible for routine clinical use. In addition, regarding the proposed CA prediction system, a clinically mature application has been developed and verified in the future digital health field.

Reliable electroencephalography (EEG) signatures of transitions between consciousness and unconsciousness under anaesthesia have not yet been identified. Herein we examined network changes using graph theoretical analysis of high-density EEG during patient-titrated propofol-induced sedation. Responsiveness was used as a surrogate for consciousness. We divided the data into five states: baseline, transition into unresponsiveness, unresponsiveness, transition into responsiveness, and recovery. Power spectral analysis showed that delta power increased from responsiveness to unresponsiveness. In unresponsiveness, delta waves propagated from frontal to parietal regions as a traveling wave. Local increases in delta connectivity were evident in parietal but not frontal regions. Graph theory analysis showed that increased local efficiency could differentiate the levels of responsiveness. Interestingly, during transitions of responsive states, increased beta connectivity was noted relative to consciousness and unconsciousness, again with increased local efficiency. Abrupt network changes are evident in the transitions in responsiveness, with increased beta band power/connectivity marking transitions between responsive states, while the delta power/connectivity changes were consistent with the fading of consciousness using its surrogate responsiveness. These results provide novel insights into the neural correlates of these behavioural transitions and EEG signatures for monitoring the levels of consciousness under sedation.

The neuronal connectivity patterns that differentiate consciousness from unconsciousness remain unclear. Previous studies have demonstrated that effective connectivity, as assessed by transcranial magnetic stimulation combined with electroencephalography (TMS–EEG), breaks down during the loss of consciousness. This study investigated changes in EEG connectivity associated with consciousness during non-rapid eye movement (NREM) sleep following parietal TMS. Compared with unconsciousness, conscious experiences during NREM sleep were associated with reduced phase-locking at low frequencies (<4 Hz). Transitivity and clustering coefficient in the delta and theta bands were also significantly lower during consciousness compared to unconsciousness, with differences in the clustering coefficient observed in scalp electrodes over parietal–occipital regions. There were no significant differences in Granger-causality patterns in frontal-to-parietal or parietal-to-frontal connectivity between reported unconsciousness and reported consciousness. Together these results suggest that alterations in spectral and spatial characteristics of network properties in posterior brain areas, in particular decreased local (segregated) connectivity at low frequencies, is a potential indicator of consciousness during sleep.