In order to seek non-propagation method to train generalized single-hidden layer feed forward neural networks, extreme learning machine was proposed, which has been proven to be an effective and ...efficient model for both multi-class classification and regression. Different from most of existing studies which consider extreme learning machine as a classifier, we make improvements on it to let it become a feature extraction model in this paper. Specifically, a discriminative extreme learning machine with supervised sparsity preserving (SPELM) model is proposed. From the hidden layer to output layer, SPELM performs as a subspace learning method by considering the discriminative as well as sparsity information of data. The sparsity information of data is identified by solving a supervised sparse representation objective. Experiments are conducted on four widely used image benchmark data sets and the classification results demonstrate the effectiveness of the proposed SPELM model.
In recent years, sleepiness during driving has become a main cause for traffic accidents. However, the fact is that we know very little yet about the electrophysiological marker for assessing diver ...sleepiness. Previous studies and our researches have shown that alpha blocking phenomenon and alpha wave attenuation-disappearance phenomenon represent two different sleepiness levels, the relaxed wakefulness and the sleep onset, respectively. This paper proposes a novel model for driver sleepiness detection based on electroencephalography (EEG) and electrooculography (EOG) signals. Our model aims to track the change in alpha waves and differentiate the two alpha-related phenomena. Continuous wavelet transform is adopted to extract features from physiological signals in both time and frequency domains. Meanwhile, Long-Short Term Memory (LSTM) network is introduced to deal with temporal information of EEG and EOG signals. To deal with insufficient physiological sample problem, generative adversarial network (GAN) is used to augment the training dataset. Experimental results indicate that the F1 score for detecting start and end points of alpha waves reaches to around 95%. And Conditional Wasserstein GAN (CWGAN) we adopted was effective in augmenting dataset and boost classifier performance. Meanwhile, our LSTM classifier achieved a mean accuracy of 98% for classifying end points of alpha waves under leave-one-subject-out cross validation.
The influences of triethanolamine (TEA) on the portlandite in hardened cement pastes (HCPs) were systematically investigated. Results show that the addition of TEA in cement pastes leads to a visible ...reduction of Ca(OH)2 (CH) content and considerably alters the morphology of CH crystals from large and parallel-stacked lamellar shape to smaller and distorted actinomorphic one. For the first time, the CH micro-crystals and even non-crystalline CH in HCPs were observed in the presence of TEA. Due to integration of CH micro-crystals in C–S–H phase, remarkable higher Ca/Si ratio of C–S–H phase was found. The formation of TEA-Ca2+complex via the interaction between Ca2+ and the oxygen atoms in TEA molecule was evidenced by the results of NMR and UV. It is believed that TEA can be introduced into the crystallization process of portlandite and thus significantly alters the morphology of CH crystals and even the content of the crystalline CH phase.
High-performance earth-abundant electrocatalysts for oxygen and hydrogen evolutions are highly desired for renewable energy but remain challenging. Herein, we have developed ultrathin NiFeP ...nanosheets, and merged the NiFeP with a 3D support of sponge-like strutted graphenes (SG). The synthesized NiFeP/SG, a porous monolith, shows efficient electrocatalytic activities for oxygen and hydrogen evolutions in alkaline electrolyte with low overpotentials of 218 and 115 mV, respectively. Using NiFeP/SG as direct catalytic electrodes, the overall water splitting requires a low cell voltage of 1.54 V to achieve a current of 10 mA cm−2. The high performances result from the shifted-up d states caused by iron incorporation, the ultrathin NiFeP, and the 3D network structure of SG. Additionally, NiFeP/SG demonstrates excellent gravimetric catalytic activities, meaningful to aerospace and portables. The material opens the way to a universal robust lightweight catalytic electrode for a variety of applications in electrochemical energy storage and conversion.
Ultrathin NiFeP nanosheets are grown on 3D self-supported strutted-graphene foams. The lightweight porous monolith is directly applied as catalytic electrodes for bifunctionally efficient electrolysis of water. A low voltage of 1.54 V of the electrolyzer drives a current of 10 mA cm−2 for the overall water splitting in alkaline media. Display omitted
•A 3D strutted graphene foam is applied as the high-surface-area conductive stable lightweight support to load catalysts.•Ultrathin NiFeP nanosheets are grown on the graphene foam, serving as self-supported binder-free porous monolithic electrodes.•NiFeP/strutted-graphene shows excellent catalytic activities and remarkable gravimetric activities for both oxygen and hydrogen evolutions.•The catalytic electrode requires a low voltage of 1.54 V to deliver 10 mA cm−2 for overall water splitting in alkaline media.
CD4+ T helper cells are well known for their role in providing critical signals during priming of cytotoxic CD8+ T lymphocyte (CTL) responses in vivo. T-cell help is required for the generation of ...primary CTL responses as well as in promoting protective CD8+ memory T-cell development. However, the role of CD4 help in the control of CTL responses at the effector stage is unknown. Here we show that fully helped effector CTLs are themselves not self-sufficient for entry into the infected tissue, but rely on the CD4+ T cells to provide the necessary cue. CD4+ T helper cells control the migration of CTL indirectly through the secretion of IFN- and induction of local chemokine secretion in the infected tissue. Our results reveal a previously unappreciated role of CD4 help in mobilizing effector CTL to the peripheral sites of infection where they help to eliminate infected cells.
Celotno besedilo
Dostopno za:
DOBA, IJS, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
•A novel low-calcium clinker with C3S2 and γ-C2S was synthesized at low string temperature.•The low-calcium clinker reduces the CO2 emission and energy consumption.•The low-calcium clinker can obtain ...a higher strength through CO2 curing.
Portland cement clinker contains more than 50% tri-calcium silicate (C3S) and is produced at ∼ 1450 °C, which releases about 850 kg CO2 per ton of clinker. Therefore, the use of dicalcium silicate (C2S) or rankinite (C3S2) in cement can be an alternative to reduce CO2 emission due to their low-calcium. In the present study, a novel low-calcium clinker containing C3S2-γ-C2S-C2AS (Ca2Al2SiO7) was synthesized, then the clinker was under carbonation curing. Experimental results show that the low-calcium clinker was synthesized at sintering temperature of 1320 °C, lower 130 °C than that of Portland cement. The clinker with non-hydraulic phases can be fully harden within 24 h and obtain a higher compressive strength. The mainly carbonation products of the clinker were calcite, aragonite and polymerization silica gels, which was attributed to the compressive strength development. In addition, calcium hydroxide and C-S-H were not detected due to the non-hydraulicity properties of C3S2 and γ-C2S. The carbonated clinker samples displayed a denser microstructure and lower pore structure. This study shows that the low-calcium and low sintering temperature clinker can obtain a better performance through carbonation curing.
The Nyström method is an efficient technique for the eigenvalue decomposition of large kernel matrices. However, to ensure an accurate approximation, a sufficient number of columns have to be ...sampled. On very large data sets, the singular value decomposition (SVD) step on the resultant data submatrix can quickly dominate the computations and become prohibitive. In this paper, we propose an accurate and scalable Nyström scheme that first samples a large column subset from the input matrix, but then only performs an approximate SVD on the inner submatrix using the recent randomized low-rank matrix approximation algorithms. Theoretical analysis shows that the proposed algorithm is as accurate as the standard Nyström method that directly performs a large SVD on the inner submatrix. On the other hand, its time complexity is only as low as performing a small SVD. Encouraging results are obtained on a number of large-scale data sets for low-rank approximation. Moreover, as the most computational expensive steps can be easily distributed and there is minimal data transfer among the processors, significant speedup can be further obtained with the use of multiprocessor and multi-GPU systems.
Multimodal signals are powerful for emotion recognition since they can represent emotions comprehensively. In this article, we compare the recognition performance and robustness of two multimodal ...emotion recognition models: 1) deep canonical correlation analysis (DCCA) and 2) bimodal deep autoencoder (BDAE). The contributions of this article are threefold: 1) we propose two methods for extending the original DCCA model for multimodal fusion: a) weighted sum fusion and b) attention-based fusion; 2) we systemically compare the performance of DCCA, BDAE, and traditional approaches on five multimodal data sets; and 3) we investigate the robustness of DCCA, BDAE, and traditional approaches on SEED-V and DREAMER data sets under two conditions: 1) adding noises to multimodal features and 2) replacing electroencephalography features with noises. Our experimental results demonstrate that DCCA achieves state-of-the-art recognition results on all five data sets: 1) 94.6% on the SEED data set; 2) 87.5% on the SEED-IV data set; 3) 84.3% and 85.6% on the DEAP data set; 4) 85.3% on the SEED-V data set; and 5) 89.0%, 90.6%, and 90.7% on the DREAMER data set. Meanwhile, DCCA has greater robustness when adding various amounts of noises to the SEED-V and DREAMER data sets. By visualizing features before and after DCCA transformation on the SEED-V data set, we find that the transformed features are more homogeneous and discriminative across emotions.
Necrosis can be induced by stimulating death receptors with tumor necrosis factor (TNF) or other agonists; however, the underlying mechanism differentiating necrosis from apoptosis is largely ...unknown. We identified the protein kinase receptor-interacting protein 3 (RIP3) as a molecular switch between TNF-induced apoptosis and necrosis in NIH 3T3 cells and found that RIP3 was required for necrosis in other cells. RIP3 did not affect RIP1-mediated apoptosis but was required for RIP1-mediated necrosis and the enhancement of necrosis by the caspase inhibitor zVAD. By activating key enzymes of metabolic pathways, RIP3 regulates TNF-induced reactive oxygen species production, which partially accounts for RIP3's ability to promote necrosis. Our data suggest that modulation of energy metabolism in response to death stimuli has an important role in the choice between apoptosis and necrosis.
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