Lithium (Li) metal is one of the most promising candidates for the anode in high‐energy‐density batteries. However, Li dendrite growth induces a significant safety concerns in these batteries. Here, ...a multifunctional separator through coating a thin electronic conductive film on one side of the conventional polymer separator facing the Li anode is proposed for the purpose of Li dendrite suppression and cycling stability improvement. The ultrathin Cu film on one side of the polyethylene support serves as an additional conducting agent to facilitate electrochemical stripping/deposition of Li metal with less accumulation of electrically isolated or “dead” Li. Furthermore, its electrically conductive nature guides the backside plating of Li metal and modulates the Li deposition morphology via dendrite merging. In addition, metallic Cu film coating can also improve thermal stability of the separator and enhance the safety of the batteries. Due to its unique beneficial features, this separator enables stable cycling of Li metal anode with enhanced Coulombic efficiency during extended cycles in Li metal batteries and increases the lifetime of Li metal anode by preventing short‐circuit failures even under extensive Li metal deposition.
Janus‐faced separator design with ultrathin copper (Cu) metal film coating onto one side surface of the conventional polyethylene separator is proposed for lithium (Li) dendrite suppression and cycling stability improvement. Enabling the separator to be electrically and ionically conductive is very effective to facilitate the electrochemical deposition/stripping of Li metal and modulate of the Li metal/electrolyte interface structure.
The abundance, genetic diversity, and crucial ecological and evolutionary roles of marine phages have prompted a large number of metagenomic studies. However, obtaining a thorough understanding of ...marine phages has been hampered by the low number of phage isolates infecting major bacterial groups other than cyanophages and pelagiphages. Therefore, there is an urgent requirement for the isolation of phages that infect abundant marine bacterial groups. In this study, we isolated and characterized HMO-2011, a phage infecting a bacterium of the SAR116 clade, one of the most abundant marine bacterial lineages. HMO-2011, which infects “ Candidatus Puniceispirillum marinum” strain IMCC1322, has an ∼55-kb dsDNA genome that harbors many genes with novel features rarely found in cultured organisms, including genes encoding a DNA polymerase with a partial DnaJ central domain and an atypical methanesulfonate monooxygenase. Furthermore, homologs of nearly all HMO-2011 genes were predominantly found in marine metagenomes rather than cultured organisms, suggesting the novelty of HMO-2011 and the prevalence of this phage type in the oceans. A significant number of the viral metagenome sequences obtained from the ocean surface were best assigned to the HMO-2011 genome. The number of reads assigned to HMO-2011 accounted for 10.3%–25.3% of the total reads assigned to viruses in seven viromes from the Pacific and Indian Oceans, making the HMO-2011 genome the most or second-most frequently assigned viral genome. Given its ability to infect the abundant SAR116 clade and its widespread distribution, Puniceispirillum phage HMO-2011 could be an important resource for marine virus research.
The goals of this study are the suggestion of a better classification method for detecting stressed states based on raw electrocardiogram (ECG) data and a method for training a deep neural network ...(DNN) with a smaller data set. We suggest an end-to-end architecture to detect stress using raw ECGs. The architecture consists of successive stages that contain convolutional layers. In this study, two kinds of data sets are used to train and validate the model: A driving data set and a mental arithmetic data set, which smaller than the driving data set. We apply a transfer learning method to train a model with a small data set. The proposed model shows better performance, based on receiver operating curves, than conventional methods. Compared with other DNN methods using raw ECGs, the proposed model improves the accuracy from 87.39% to 90.19%. The transfer learning method improves accuracy by 12.01% and 10.06% when 10 s and 60 s of ECG signals, respectively, are used in the model. In conclusion, our model outperforms previous models using raw ECGs from a small data set and, so, we believe that our model can significantly contribute to mobile healthcare for stress management in daily life.
This paper proposes a novel robust attitude estimation algorithm for a small unmanned aerial vehicle (UAV) in the absence of GPS measurements. A synthetic sideslip angle (SSA) measurement formulated ...for use under the zeroangle assumption is newly proposed for a UAV without angle-of-attack (AOA)/SSA sensors to enhance the state estimation performance during a GPS outage. In addition, the nongravitational acceleration is estimated using the proposed Kalman filter and is then subtracted from the raw acceleration to yield a reliable gravity estimate. Then, a fuzzy-logic-aided adaptive measurement covariance matching algorithm is devised to adaptively reduce the weight given to disturbed acceleration and magnetic field measurements in the attitude estimation, yielding the fuzzy adaptive error-state Kalman filter (FAESKF) algorithm. Experimental flight results demonstrate that the proposed FAESKF algorithm achieves a remarkable improvement in attitude estimation compared to the conventional algorithm.
The joint angle during gait is an important indicator, such as injury risk index, rehabilitation status evaluation, etc. To analyze gait, inertial measurement unit (IMU) sensors have been used in ...studies and continuously developed; however, they are difficult to utilize in daily life because of the inconvenience of having to attach multiple sensors together and the difficulty of long-term use due to the battery consumption required for high data sampling rates. To overcome these problems, this study propose a multi-joint angle estimation method based on a long short-term memory (LSTM) recurrent neural network with a single low-frequency (23 Hz) IMU sensor. IMU sensor data attached to the lateral shank were measured during overground walking at a self-selected speed for 30 healthy young persons. The results show a comparatively good accuracy level, similar to previous studies using high-frequency IMU sensors. Compared to the reference results obtained from the motion capture system, the estimated angle coefficient of determination (R2) is greater than 0.74, and the root mean square error and normalized root mean square error (NRMSE) are less than 7° and 9.87%, respectively. The knee joint showed the best estimation performance in terms of the NRMSE and R2 among the hip, knee, and ankle joints.
Dopamine, which can be electrochemically oxidized to polydopamine on cathode surface, was introduced as an electrolyte additive for high-voltage lithium-ion batteries (LIBs). The addition of 0.1 wt % ...dopamine to the electrolyte led to the formation of a polydopamine-containing layer on the cathode, thereby resulting in suppression of the oxidative decomposition of the electrolyte during high-voltage operation (up to 4.5 V) of a LiNi1/3Co1/3Mn1/3O2/artificial graphite cell. The addition of dopamine to the electrolyte improved the capacity retention of the cell from 136 to 147 mAh g–1 after 100 cycles at a rate of 1 C and a cutoff voltage of 4.5 V, while the cycle performance and rate capability with a cutoff voltage of 4.3 V were comparable to those of the cell without dopamine. Further evidence of the positive impact of dopamine on high-voltage LIBs was the lower DC-IRs and AC impedances, as well as the retention of the cathode morphology even after operation at 4.5 V.
A highly adhesive and thermally stable copolyimide (P84) that is soluble in organic solvents is newly applied to silicon (Si) anodes for high energy density lithium-ion batteries. The Si anodes with ...the P84 binder deliver not only a little higher initial discharge capacity (2392 mAh g–1), but also fairly improved Coulombic efficiency (71.2%) compared with the Si anode using conventional polyvinylidene fluoride binder (2148 mAh g–1 and 61.2%, respectively), even though P84 is reduced irreversibly during the first charging process. This reduction behavior of P84 was systematically confirmed by cyclic voltammetry and Fourier-transform infrared analysis in attenuated total reflection mode of the Si anodes at differently charged voltages. The Si anode with P84 also shows ultrastable long-term cycle performance of 1313 mAh g–1 after 300 cycles at 1.2 A g–1 and 25 °C. From the morphological analysis on the basis of scanning electron microscopy and optical images and of the electrode adhesion properties determined by surface and interfacial cutting analysis system and peel tests, it was found that the P84 binder functions well and maintains the mechanical integrity of Si anodes during hundreds of cycles. As a result, when the loading level of the Si anode is increased from 0.2 to 0.6 mg cm–2, which is a commercially acceptable level, the Si anode could deliver 647 mAh g–1 until the 300th cycle, which is still two times higher than the theoretical capacity of graphite at 372 mAh g–1.
Nasopharyngeal swabs (NPSs) are being widely used as specimens for multiplex real-time reverse transcription (RT)-PCR for respiratory virus detection. However, it remains unclear whether NPS ...specimens are optimal for all viruses targeted by multiplex RT-PCR. In addition, the procedure to obtain NPS specimens causes coughing in most patients, which possibly increases the risk of nosocomial spread of viruses. In this study, paired NPS and saliva specimens were collected from 236 adult male patients with suspected acute respiratory illnesses. Specimens were tested for 16 respiratory viruses by multiplex real-time RT-PCR. Among the specimens collected from the 236 patients, at least 1 respiratory virus was detected in 183 NPS specimens (77.5%) and 180 saliva specimens (76.3%). The rates of detection of respiratory viruses were comparable for NPS and saliva specimens (P = 0.766). Nine virus species and 349 viruses were isolated, 256 from NPS specimens and 273 from saliva specimens (P = 0.1574). Adenovirus was detected more frequently in saliva samples (P < 0.0001), whereas influenza virus type A and human rhinovirus were detected more frequently in NPS specimens (P = 0.0001 and P = 0.0289, respectively). The possibility of false-positive adenovirus detection from saliva samples was excluded by direct sequencing. In conclusion, neither of the sampling methods was consistently more sensitive than the other. We suggest that these cost-effective methods for detecting respiratory viruses in mixed NPS-saliva specimens might be valuable for future studies.
The ventilatory threshold (VT) marks the transition from aerobic to anaerobic metabolism and is used to assess cardiorespiratory endurance. A conventional way to assess VT is cardiopulmonary exercise ...testing, which requires a gas analyzer. Another method for measuring VT involves calculating the heart rate variability (HRV) from an electrocardiogram (ECG) by computing the variability of heartbeats. However, the HRV method has some limitations. ECGs should be recorded for at least 5 minutes to calculate the HRV, and the result may depend on the utilized ECG preprocessing algorithms.
To overcome these problems, we developed a deep learning-based model consisting of long short-term memory (LSTM) and convolutional neural network (CNN) for a lead II ECG. Variables reflecting subjects' physical characteristics, as well as ECG signals, were input into the model to estimate VT. We applied joint optimization to the CNN layers to generate an informative latent space, which was fed to the LSTM layers. The model was trained and evaluated on two datasets, one from the Bruce protocol and the other from a protocol including multiple tasks (MT).
Acceptable performances (mean and 95% CI) were obtained on the datasets from the Bruce protocol (-0.28-1.91,1.34 ml/min/kg) and the MT protocol (0.07-3.14,3.28 ml/min/kg) regarding the differences between the predictions and labels. The coefficient of determination, Pearson correlation coefficient, and root mean square error were 0.84, 0.93, and 0.868 for the Bruce protocol and 0.73, 0.97, and 3.373 for the MT protocol, respectively.
The results indicated that it is possible for the proposed model to simultaneously assess VT with the inputs of successive ECGs. In addition, from ablation studies concerning the physical variables and the joint optimization process, it was demonstrated that their use could boost the VT assessment performance of the model. The proposed model enables dynamic VT estimation with ECGs, which could help with managing cardiorespiratory fitness in daily life and cardiovascular rehabilitation in patients.
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