Optical diagnosis-based combustion experiments were conducted to investigate the characteristics of cavity assisted hydrogen jet combustion in a supersonic flow with a total pressure of 1.6 MPa, a ...total temperature of 1486 K, and a Mach number of 2.52, simulating flight Mach 6 conditions. A supersonic combustor with a constant cross-sectional area was employed with several cavity configurations, fueling schemes and equivalence ratios. It was found that stable combustion could not be obtained without a cavity, indicating that pure jet-wake stabilized combustion could not be achieved and the cavity acted as a flameholder. Three combustion modes were observed for the cavity assisted hydrogen jet combustion: cavity assisted jet-wake stabilized combustion, cavity shear-layer stabilized combustion, and combined cavity shear-layer/recirculation stabilized combustion. The cavity assisted jet-wake stabilized combustion was observed to be the most unstable mode, accompanied by intermittent blowoff under the present conditions, while the combined cavity shear-layer/recirculation stabilized combustion mode seemed to be the most robust one.
•Combustion modes in a supersonic combustor are investigated experimentally.•Three combustion modes are observed for cavity assisted hydrogen jet combustion.•Cavity assisted jet-wake stabilized combustion is the most unstable mode.•Combined cavity shear-layer/recirculation stabilized combustion is the most robust.
Considering the existing issues of traditional blood pressure (BP) measurement methods and non-invasive continuous BP measurement techniques, this study aims to establish the systolic BP and ...diastolic BP estimation models based on machine learning using pulse transit time and characteristics of pulse waveform. In the process of model construction, the mean impact value method was introduced to investigate the impact of each feature on the models and the genetic algorithm was introduced to implement parameter optimization. The experimental results showed that the proposed models could effectively describe the nonlinear relationship between the features and BP and had higher accuracy than the traditional methods with the error of 3.27 ± 5.52 mmHg for systolic BP and 1.16 ± 1.97 mmHg for diastolic BP. Moreover, the estimation errors met the requirements of the Advancement of Medical Instrumentation and British Hypertension Society criteria. In conclusion, this study was helpful in promoting the practical application of methods for non-invasive continuous BP estimation models.
•Opinions of others result in altered neural correlates of valuation.•Deviations from group norms consistently engage dorsal pMFC and AI.•Agreement with normative opinions consistently activates ...VS.•Deviation-related activation of dorsal pMFC predicts conforming behaviors.•Disagreement- and unfairness-related contrasts converged in pMFC and AI.
People often align their behaviors with group opinions, known as social conformity. Many neuroscience studies have explored the neuropsychological mechanisms underlying social conformity. Here we employed a coordinate-based meta-analysis on neuroimaging studies of social conformity with the purpose to reveal the convergence of the underlying neural architecture. We identified a convergence of reported activation foci in regions associated with normative decision-making, including ventral striatum (VS), dorsal posterior medial frontal cortex (dorsal pMFC), and anterior insula (AI). Specifically, consistent deactivation of VS and activation of dorsal pMFC and AI are identified when people’s responses deviate from group opinions. In addition, the deviation-related responses in dorsal pMFC predict people’s conforming behavioral adjustments. These are consistent with current models that disagreement with others might evoke “error” signals, cognitive imbalance, and/or aversive feelings, which are plausibly detected in these brain regions as control signals to facilitate subsequent conforming behaviors. Finally, group opinions result in altered neural correlates of valuation, manifested as stronger responses of VS to stimuli endorsed than disliked by others.
Single image dehazing is a challenging ill-posed problem due to the severe information degeneration. However, existing deep learning based dehazing methods only adopt clear images as positive samples ...to guide the training of dehazing network while negative information is unexploited. Moreover, most of them focus on strengthening the dehazing network with an increase of depth and width, leading to a significant requirement of computation and memory. In this paper, we propose a novel contrastive regularization (CR) built upon contrastive learning to exploit both the information of hazy images and clear images as negative and positive samples, respectively. CR ensures that the restored image is pulled to closer to the clear image and pushed to far away from the hazy image in the representation space.Furthermore, considering trade-off between performance and memory storage, we develop a compact dehazing network based on autoencoder-like (AE) framework. It involves an adaptive mixup operation and a dynamic feature enhancement module, which can benefit from preserving information flow adaptively and expanding the receptive field to improve the network's transformation capability, respectively. We term our dehazing network with autoencoder and contrastive regularization as AECR-Net. The extensive experiments on synthetic and real-world datasets demonstrate that our AECR-Net surpass the state-of-the-art approaches. The code is released in https://github.com/GlassyWu/AECR-Net.
Negative emotions play a dominant role in daily human life, and mentalizing and empathy are also basic sociability in social life. However, little is known regards the neurophysiological pattern of ...negative experiences in immersive environments and how people with different sociabilities respond to the negative emotional stimuli at behavioral and neural levels. The present study investigated the neurophysiological representation of negative affective experiences and whether such variations are associated with one's sociability. To address this question, we examined four types of negative emotions that frequently occurred in real life: angry, anxious, fearful, and helpless. We combined naturalistic neuroimaging under virtual reality, multimodal neurophysiological recording, and behavioral measures. Inter-subject representational similarity analysis was conducted to capture the individual differences in the neurophysiological representations of negative emotional experiences. The behavioral and neurophysiological indices revealed that although the emotion ratings were uniquely different, a similar electroencephalography response pattern across these negative emotions was found over the parieto-occipital electrodes. Furthermore, the neurophysiological representations indeed reflected interpersonal variations regarding mentalizing and empathic abilities. Our findings yielded a common pattern of neurophysiological responses toward different negative affective experiences in VR. Moreover, the current results indicate the potential of taking a sociability perspective for understanding the interpersonal variations in the neurophysiological representation of emotion.
Authorship attribution is to extract features to identify authors of anonymous documents. Many previous works on authorship attribution focus on statistical style features (e.g., sentence/word ...length), content features (e.g., frequent words, n-grams). Modeling these features by regression or some transparent machine learning methods gives a portrait of the authors’ writing style. But these methods do not capture the syntactic (e.g., dependency relationship) or semantic (e.g., topics) information. In recent years, some researchers model syntactic trees or latent semantic information by neural networks. However, few works take them together. In this paper, we propose a novel Multi-Channel Self-Attention Network (MCSAN) incorporating both the inter-channel and inter-positional interaction to extract n-grams of the characters, words, parts of speech (POS), phrase structures, dependency relationships, and topics from multiple dimensions (style, content, syntactic and semantic features) to distinguish different authors. And then we incorporate these extracted features with logistic regression (LR) to do experiments, and the experimental results manifest that our extracted features are effective. Our methods improve 2.1% and 3.0% on CCAT10 and CCAT50, respectively, comparing with state-of-the-art models.
•N-Gram attention Network for feature extraction is proposed.•Multi-level linguistic features are applied to the task of Authorship attribution.•Combining those extracted features with Logistic Regression algorithm and comparing with baselines, experimental results show our proposed method is effective.•Finally, we analyzed each type of feature, especially the syntactic structure.
, commonly known as guava root-knot nematode, poses risk due to its widespread distribution and extensive host range. This species is recognized as the most virulent root-knot nematode (RKN) species ...because it can emerge and breed in plants that have resistance to other tropical RKNs. They cause chlorosis, stunting, and yield reductions in host plants by producing many root galls. It is extremely challenging for farmers to diagnose due to the symptoms' resemblance to nutritional inadequacies. This pathogen has recently been considered a significant worldwide threat to agricultural production. It is particularly challenging to diagnose a
due to the similarities between this species and other RKN species. Identified using traditional morphological and molecular techniques, which is a crucial first in integrated management. Chemical control, biological control, the adoption of resistant cultivars, and cultural control have all been developed and effectively utilized to combat root-knot nematodes in the past. The object of this study was to get about the geographical distribution, host plants, symptoms, identification, and control techniques of
and recommend future initiatives to progress its management.
With the passage of time and indiscreet usage of insecticides on crops, aphids are becoming resistant to their effect. The different classes of insecticides, including organophosphates, carbamates, ...pyrethroids and neonicotinoids, have varied effects on insects. Furthermore, the molecular effects of these insecticides in aphids, including effects on the enzymatic machinery and gene mutation, are resulting in aphid resistance to the insecticides. In this review, we will discuss how aphids are affected by the overuse of pesticides, how resistance appears, and which mechanisms participate in the resistance mechanisms in various aphid species as significant crop pests. Gene expression studies were analyzed using the RNA-Seq technique. The stress-responsive genes were analyzed, and their expression in response to insecticide administration was determined. Putative insecticide resistance-related genes, cytochrome P450, glutathione S-transferase, carboxylesterase CarEs, ABC transporters, cuticle protein genes, and trypsin-related genes were studied. The review concluded that if insecticide-susceptible aphids interact with ample dosages of insecticides with sublethal effects, this will result in the upregulation of genes whose primary role is to detoxify insecticides. In the past decade, certain advancements have been observed regarding insecticide resistance on a molecular basis. Even so, not much is known about how aphids detoxify the insecticides at molecular level. Thus, to attain equilibrium, it is important to observe the manipulation of pest and insect species with the aim of restoring susceptibility to insecticides. For this purpose, this review has included critical insights into insecticide resistance in aphids.
Cardiovascular disease (CVD) is the leading cause of death globally, except Africa, and poses a severe health burden worldwide. Both in vitro and in vivo studies have demonstrated the protective ...effects of myricetin for preventing CVD. For this review, we have assessed the literature from 2009 to 2019 at home and abroad to uncover the protective roles of myricetin for preventing CVD. Myricetin exhibits cardioprotective, anti-hypertensive, anti-atherosclerotic, anti-hyperglycemic, and anti-hyperlipidemic effects. In addition, myricetin may alleviate some of the complications caused by adult-onset diabetes. The combined functions of myricetin allow for the prevention of CVD. This review describes the possible therapeutic benefits of myricetin, along with its potential mechanisms of action, to support the clinical use of the myricetin for the prevention of CVD.
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