Multi-dimensional metal oxides have become a promising alternative electrode material for supercapacitors due to their inherent large surface area. Herein, P-doped NiCo2O4/NiMoO4 multi-dimensional nanostructures are synthesized on carbon clothes (CC) with a continuous multistep strategy. Especially, P has the best synergistic effect with transition metals, such as optimal deprotonation energy and OH- adsorption energy, which can further enhance electrochemical reaction activity. For the above reasons, the P-NiCo2O4/NiMoO4@CC electrode exhibits an ultra-high specific capacitance of 2334.0 F g-1 at 1 A g-1. After 1500 cycles at a current density of 10 A g-1, its specific capacity still maintains 93.7%. Besides, a P-NiCo2O4/NiMoO4@CC//activated carbon device (hybrid supercapacitor or device) was also prepared with a maximum energy density of 45.1 Wh kg-1 at a power density of 800 W kg-1. In particular, the capacity retention rate is still 89.97% after 8000 cycles due to its excellent structural stability. Our work demonstrates the vast potential of multi-dimensional metal oxides in energy storage.Shape-controlled synthesis is essential for functional nanomaterials, allowing deeper insights intothe relationship between the structures and the catalytic properties. Synthesis of nanocrystals with particular morphologies are usually studied independently among various synthetic methods, those underline that different surface capping ligands or shape-directing agents bring about disparate shapes. However, a single quantitative parameter method is still lacking to realize precise control of well-defined morphology nanocrystals, especially anisotropic structures, which is essential to understanding the growth process of nanocrystals. Herein, we proposed a single-parameter-tuned synthesis strategy for preparation of shape-controlled gold nanocrystals by regulating the amount of iron carbonyl, by which we produced highly monodisperse Au nanocrystals with various shapes in organic phase including nanoplates (diameter of 16.02 ± 1.13 nm and thickness of 5.35 ± 0.58 nm), nanorods (length of 37.53 ± 3.73 nm and width of 5.26 ± 0.37 nm) and nanospheres (diameter of 8.26 ± 0.38 nm). The single-parameter-tuned method reveals the dual roles of iron carbonyl for controlling the shapes of gold nanocrystals including reductant and oxidative etchant and empowers versatility in synthetic methodology for other noble metals. Moreover, catalytic activity shifting in shapes of nanocrystals was revealed based on the reduction of 4-nitrophenol, showing that the as-synthesized Au nanoplates displayed the enhanced catalytic performance with the lowest activation energy. Our work provides a brand-new pathway for shape-controlled synthesis of noble-metal nanocrystals and has a strong practical value in application fields.Most recent research on human tool use highlighted how people might integrate multiple sources of information through different neurocognitive systems to exploit the environment for action. This mechanism of integration is known as „action reappraisal”. In the present eye-tracking study, we further tested the action reappraisal idea by devising a word-priming paradigm to investigate how semantically congruent (e.g., „nail”) vs. semantically incongruent words (e.g., „jacket”) that preceded the vision of tools (e.g., a hammer) may affect participants’ visual exploration of them. We found an implicit modulation of participants’ temporal allocation of visuospatial attention as a function of the object-word consistency. Indeed, participants tended to increase over time their fixations on tools’ manipulation areas under semantically congruent conditions. Conversely, participants tended to concentrate their visual-spatial attention on tools’ functional areas when inconsistent object-word pairs were presented. These results support and extend the information-integrated perspective of the action reappraisal approach. Also, these findings provide further evidence about how higher-level semantic information may influence tools’ visual exploration.Embedded feature selection algorithms, such as support vector machine based recursive feature elimination (SVM-RFE), have proven to be effective for many real applications. However, due to the model selection problem, SVM-RFE naturally suffers from a heavy computational burden as well as high computational complexity. To solve these issues, this paper proposes using an optimized extreme learning machine (OELM) model instead of SVM. This model, referred to as OELM-RFE provides an efficient active set solver for training the OELM algorithm. We also present an effective alpha seeding algorithm to efficiently solve successive quadratic programming (QP) problems inherent in OELM. One of the salient characteristics of OELM-RFE is that it has only one tuning parameter the penalty constant C. Experimental results from work on benchmark datasets show that OELM-RFE tends to have higher prediction accuracy than SVM-RFE, and requires fewer model selection efforts. In addition, the alpha seeding method works better on more datasets.Many schools and universities have seen a significant increase in the spread of COVID-19. As such, a number of non-pharmaceutical interventions have been proposed including distancing requirements, surveillance testing, and updating ventilation systems. Unfortunately, there is limited guidance for which policy or set of policies are most effective for a specific school system. We develop a novel approach to model the spread of SARS-CoV-2 quanta in a closed classroom environment that extends traditional transmission models that assume uniform mixing through air recirculation by including the local spread of quanta from a contagious source. In addition, the behavior of students with respect to guideline compliance was modeled through an agent-based simulation. Estimated infection rates were on average lower using traditional transmission models compared to our approach. Further, we found that although ventilation changes were effective at reducing mean transmission risk, it had much less impact than distancing practices. Duration of the class was an important factor in determining the transmission risk. For the same total number of semester hours for a class, delivering lectures more frequently for shorter durations was preferable to less frequently with longer durations. Finally, as expected, as the contact tracing level increased, more infectious students were identified and removed from the environment and the spread slowed, though there were diminishing returns. These findings can help provide guidance as to which school-based policies would be most effective at reducing risk and can be used in a cost/comparative effectiveness estimation study given local costs and constraints.Sleep apnea is a common symptomatic disease affecting nearly 1 billion people around the world. The gold standard approach for determining the severity of sleep apnea is full-night polysomnography conducted in the laboratory, which is very costly and cumbersome. In this work, we propose a novel scalogram-based convolutional neural network (SCNN) to detect obstructive sleep apnea (OSA) using single-lead electrocardiogram (ECG) signals. Firstly, we use continuous wavelet transform (CWT) to convert ECG signals into conventional scalograms. In parallel, we also apply empirical mode decomposition (EMD) to the signals to find correlated intrinsic mode functions (IMFs) and then apply CWT on the IMFs to obtain hybrid scalograms. Finally, we train a lightweight CNN model on these scalograms to extract deep features for OSA detection. Experiments on the benchmark Apnea-ECG dataset demonstrate that our proposed model results in an accuracy of 94.30%, sensitivity 94.30%, specificity 94.51%, and F1-score 95.85% in per-segment classification. Our model also achieves an accuracy of 81.86%, sensitivity 71.62%, specificity 86.05%, and F1-score 69.63% for UCDDB dataset. Furthermore, our model achieves an accuracy of 100.00% in per-recording classification for Apnea-ECG dataset. The experimental results outperform the existing OSA detection approaches using ECG signals.Organoid, an in vitro 3D culture, has extremely high similarity with its source organ or tissue, which creates a model in vitro that simulates the in vivo environment. Organoids have been extensively studied in cell biology, precision medicine, drug toxicity, efficacy tests, etc., which have been proven to have high research value. Periodic observation of organoids in microscopic images to obtain morphological or growth characteristics is essential for organoid research. It is difficult and time-consuming to perform manual screens for organoids, but there is no better solution in the prior art. In this paper, we established the first high-throughput organoid image dataset for organoids detection and tracking, which experienced experts annotate in detail. Moreover, we propose a novel deep neural network (DNN) that effectively detects organoids and dynamically tracks them throughout the entire culture. We divided our solution into two steps First, the high-throughput sequential images are processed frame by frame to detect all organoids; Second, the similarities of the organoids in the adjacent frames are computed, and the organoids on the adjacent frames are matched in pairs. With the help of our proposed dataset, our model achieves organoids detection and tracking with fast speed and high accuracy, effectively reducing the burden on researchers. To our knowledge, this is the first exploration of applying deep learning to organoid tracking tasks. Experiments have demonstrated that our proposed method achieved satisfactory results on organoid detection and tracking, verifying the great potential of deep learning technology in this field.Here it was investigated how oligonucleotide retention and selectivity factors are affected by electrostatic and non-electrostatic interactions in ion pair chromatography. A framework was derived describing how selectivity depends on the electrostatic potential generated by the ion-pair reagent concentration, co-solvent volume fraction, charge difference between the analytes, and temperature. Isocratic experiments verified that, in separation problems concerning oligonucleotides of different charges, selectivity increases with increasing surface potential and analyte charge difference and with decreasing co-solvent volume fraction and temperature. For analytes of the same charge, for example, diastereomers of phosphorothioated oligonucleotides, selectivity can be increased by decreasing the co-solvent volume fraction or the temperature and has only a minor dependency on the ion-pairing reagent concentration. An important observation is that oligonucleotide retention is driven predominantly by electrostatic interaction generated by the adsorption of the ion-pairing reagent. We therefore compared classical gradient elution in which the co-solvent volume fraction increases over time versus gradient elution with a constant co-solvent volume fraction but with decreasing ion-pair reagent concentration over time. Both modes decrease the electrostatic potential. Oligonucleotide selectivity was found to increase with decreasing ion-pairing reagent concentration. The two elution modes were finally applied to two different model antisense oligonucleotide separation problems, and it was shown that the ion-pair reagent gradient increases the selectivity of non-charge-based separation problems while maintaining charge-difference-based selectivity.