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Most of the dimensionality reduction algorithms assume that data are independent and identically distributed (i.i.d.). In real-world applications, however, sometimes there exist relationships between data. Some relational learning methods have been proposed, but those with discriminative relationship analysis are lacking yet, as important supervisory information is usually ignored. In this paper, we propose a novel and general framework, called relational Fisher analysis (RFA), which successfully integrates relational information into the dimensionality reduction model. For nonlinear data representation learning, we adopt the kernel trick to RFA and propose the kernelized RFA (KRFA). In addition, the convergence of the RFA optimization algorithm is proved theoretically. By leveraging suitable strategies to construct the relational matrix, we conduct extensive experiments to demonstrate the superiority of our RFA and KRFA methods over related approaches. [ABSTRACT FROM AUTHOR]

The cribriform-morular variant of papillary thyroid carcinoma (CMV-PTC) is now designated as morular cribriform thyroid carcinoma (CMTC) according to the 5th edition of the World Health Organization (WHO) Classification of Thyroid Tumors. CMTC can appear within a familial adenomatous polyposis (FAP) or be sporadic. We report the first case of a young female patient in China who was diagnosed with FAP and CMTC with a mutation in exon 16 of the APC gene underlying the disease. The main purpose of this case report is to provide a special pathological type of thyroid tumors, which is expected to be helpful for clinical work in the future. [ABSTRACT FROM AUTHOR]

In recent years, circular RNAs (circRNAs) have been found to play an essential regulatory role in hepatocellular carcinoma (HCC) through various mechanisms, particularly the endogenous competitive RNA (ceRNA) mechanism. Therefore, it is significant to explore the circRNAs in hepatoma. In this study, we constructed the ceRNA and survival network using Cytoscape. We also used R, Perl software, and multiple online databases and platforms, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), to perform overall survival, immune cell infiltration, immune checkpoints, pathway activity, and anticancer drug sensitivity analysis of the genes. Finally, the receiver operator characteristic curve (ROC) analysis was performed to identify the diagnosis value of the genes. KEGG analysis revealed the T cell receptor signaling pathway as the main enrichment pathway. A total of 29 genes related to survival and prognosis were screened out. The findings suggest that ZNF544, WDR76, ACTG1, RASSF3, E2F3, ASRGL1, and POGK are associated with multilevel immune cell infiltration. Additionally, immune checkpoint analysis screened out the ACTG1, E2F3, RASSF3, and WDR76. It was also revealed that the WDR76, E2F3, ASRGL1, and POGK mainly activated the cell cycle and DNA damage response (DDR) pathway. The results suggest that the sensitivity toward trametinib, refametinib (RDEA119), and selumetinib correlates to the expression of WDR76. ROC analysis showed that the area under the curve (AUC) of all genes in the regulatory axis was greater than 0.7. The identified hsa_circ_0000417/hsa_circ_0002688/hsa_circ_0001387--hsa-miR-199a-5p--WDR76 regulatory axis may provide new insights into the progression, clinical diagnosis, and treatment of HCC. [ABSTRACT FROM AUTHOR]

Gated recurrent unit (GRU) is a variant of the recurrent neural network (RNN). It has been widely used in many applications, such as handwriting recognition and natural language processing. However, GRU can only memorize the sequential information, but lacks the capability of adaptively paying attention to important parts in the sequences. In this paper, we propose a novel RNN model, called recurrent attention unit (RAU), which can seamlessly integrate the attention mechanism into the interior of the GRU cell by adding an attention gate. The attention gate enhances the ability of RAU to remember long-term information and pay attention to important parts in the sequential data. Extensive experiments on adding problem, image classification, sentiment classification and language modeling show that RAU consistently outperforms GRU and other related models. [ABSTRACT FROM AUTHOR]

• The neutron energy response of Cs 2 7LiYCl 6 :Ce (CLYC-7) scintillation detector was investigated, and the figures-of-merit (FOM), response function, and neutron energy resolution at different neutron energies were obtained. • The Monte Carlo method was used to simulate the particle transport within the CLYC-7 scintillator and investigate the energy response curves of different particles. • The response function was applied to the CLYC-7 scintillation detector, and it was used to measure the D-D fusion neutron spectrum on EAST. During plasma experiments on EAST (Experimental Advanced Superconducting Tokamak), the discharges heated with neutral beam injection (NBI) for plasma results in the production of 2.45 MeV deuterium-deuterium (D-D) fusion neutrons. In order to measure the energy spectrum of D-D fusion neutrons, a new Cs 2 7LiYCl6:Ce (CLYC-7) crystal with 7Li-enrichment has been developed and fabricated into a CLYC-7 scintillation detector. Before utilizing this detector for studying neutron energy spectra, it is crucial to explore the response function of the detector within a relevant neutron energy range. Therefore, the detector has been experimented using the 4.5 MeV electrostatic accelerator at Peking University to investigate its response to fast neutrons, energy resolution, and pulse discrimination capability in the neutron energy range 2.07 MeV–5.51 MeV. The neutron reactions occurring within the CLYC-7 scintillator were simulated using MCNPX, focusing on the reactions 35Cl(n,p) and 35Cl(n,α), as well as the energy response curve and the contributions of different particles (protons, alphas) to the neutron spectrum. The experimental results were successfully validated on EAST, where the 2.45 MeV fusion neutron energy spectrum was measured. That demonstrates the ability of the CLYC-7 scintillation detector to diagnose the fusion neutron energy spectrum for deuterium plasma discharge. [ABSTRACT FROM AUTHOR]

The rapid growth of Internet technology renders intrusion detection an important research topic in the field of pattern recognition. Considering that traffic data relate to not only temporal information, but also attack frequency, this paper presents a novel deep learning framework termed the multi-frequency intrusion detection network (MF-Net). MF-Net regards the pattern of Internet traffic as a superposition of sequential data with various frequencies, and is able to recognize the multi-frequency nature of network traffic data. The core of MF-Net is the multi-frequency LSTM (MF-LSTM) and multi-frequency transformer (MF-Transformer) module, both of which consist of high-frequency and low-frequency layers. In comparison with other state-of-the-art approaches on 4 public datasets, namely UNSW-NB15, KDD Cup 99, NSL-KDD and CICIDS 2017, as well as an IPv6 traffic dataset we created, MF-Net has shown better result in both binary and multi-class classification, which demonstrates the superiority of MF-Net over other compared approaches on network traffic intrusion detection. [Display omitted] • MF-Net captures multiple frequencies features with both long-term and short-term dependencies. • Both MF-LSTM and MF-Transformer detect network intrusion data with different frequencies. • MF-Net achieve state-of-the-art performance on 5 network intrusion datasets. [ABSTRACT FROM AUTHOR]

Attention has arguably become one of the most important concepts in the deep learning field. It is inspired by the biological systems of humans that tend to focus on the distinctive parts when processing large amounts of information. With the development of deep neural networks, attention mechanism has been widely used in diverse application domains. This paper aims to give an overview of the state-of-the-art attention models proposed in recent years. Toward a better general understanding of attention mechanisms, we define a unified model that is suitable for most attention structures. Each step of the attention mechanism implemented in the model is described in detail. Furthermore, we classify existing attention models according to four criteria: the softness of attention, forms of input feature, input representation, and output representation. Besides, we summarize network architectures used in conjunction with the attention mechanism and describe some typical applications of attention mechanism. Finally, we discuss the interpretability that attention brings to deep learning and present its potential future trends. [ABSTRACT FROM AUTHOR]

In recent years, research on image generation has been developing very fast. The generative adversarial network (GAN) emerges as a promising framework, which uses adversarial training to improve the generative ability of its generator. However, since GAN and most of its variants use randomly sampled noises as the input of their generators, they have to learn a mapping function from a whole random distribution to the image manifold. As the structures of the random distribution and the image manifold are generally different, this results in GAN and its variants difficult to train and converge. In this paper, we propose a novel deep model called generative adversarial networks with decoder–encoder output noises (DE-GANs), which take advantage of both the adversarial training and the variational Bayesian inference to improve GAN and its variants on image generation performances. DE-GANs use a pre-trained decoder–encoder architecture to map the random noise vectors to informative ones and feed them to the generator of the adversarial networks. Since the decoder–encoder architecture is trained with the same data set as the generator, its output vectors, as the inputs of the generator, could carry the intrinsic distribution information of the training images, which greatly improves the learnability of the generator and the quality of the generated images. Extensive experiments demonstrate the effectiveness of the proposed model, DE-GANs. • We propose a new model called GANs with decoder-encoder output noises (DE-GANs). • The decoder-encoder structure learns informative noises as the inputs of DE-GANs. • The informative noises carry the intrinsic information of the image manifold. • DE-GANs converge fast and can generate high quality images. [ABSTRACT FROM AUTHOR]

Deep neural networks have gained state-of-the-art results in many applications, such as pattern recognition and computer vision. However, most of the deep neural networks are designed manually by researchers. This architecture design process is generally time consuming and needs much expertise. Hence, automatic neural network design becomes an important issue. In this paper, we propose a novel method, called DNA computing inspired networks design (DNAND), to automatically learn high performance deep networks. In DNAND, we use DNA strands to represent blocks of a model, and these DNA strands are reacted to construct the overall networks according to the base pairing principle. We also present the killing strategy, with which we stop training "bad" models if they fail to reach the specific accuracy threshold on the validation set, so as to reduce the computational cost and accelerate the learning process. Extensive experiments on image classification and detection data sets demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Image generation is a long-standing problem in the machine learning and computer vision areas. In order to generate images with high diversity, we propose a novel model called generative adversarial networks with mixture of t-distributions noise (tGANs). In tGANs, the latent generative space is formulated using a mixture of t-distributions. Particularly, the parameters of the components in the mixture of t-distributions can be learned along with others in the model. To improve the diversity of the generated images in each class, each noise vector and a class codeword are concatenated as the input of the generator of tGANs. In addition, a classification loss is added to both the generator and the discriminator losses to strengthen their performances. We have conducted extensive experiments to compare tGANs with a state-of-the-art pixel by pixel image generation approach, pixelCNN, and related GAN-based models. The experimental results and statistical comparisons demonstrate that tGANs perform significantly better than pixleCNN and related GAN-based models for diverse image generation. • A novel model called tGANs for diverse image generation is proposed. • The noise vectors of tGANs are sampled from a mixture of t-distributions. • The parameters of the t-distributions can be learned together with others in tGANs. • A classification loss is added in the generator and discriminator losses of tGANs. • Experiments show that tGANs outperform related models on diverse image generation. [ABSTRACT FROM AUTHOR]

Metric and similarity learning are important approaches to classification and retrieval. To efficiently learn a distance metric or a similarity function, online learning algorithms have been widely applied. In general, however, existing online metric and similarity learning algorithms have limited performance in real-world classification and retrieval applications. In this paper, we introduce a convergent online metric learning model named scalable large margin online metric learning (SLMOML). SLMOML belongs to the passive-aggressive learning family. At each step, it adopts the LogDet divergence to maintain the closeness between two successively learned Mahalanobis matrices, and utilizes the hinge loss to enforce a large margin between relatively dissimilar samples. More importantly, the Mahalanobis matrix can be updated by closed-form solution at each step. Furthermore, if the initial matrix is positive semi-definite (PSD), the learned matrices in the following steps are always PSD. Based on the Karush–Kuhn–Tucker conditions and the built equivalence between the passive-aggressive learning family and the Bregman projections, we have proved the global convergence of SLMOML. Extensive experiments on classification and retrieval tasks demonstrate the effectiveness and efficiency of SLMOML. [ABSTRACT FROM AUTHOR]

Abstract Random forests algorithms have been widely used in many classification and regression applications. However, the theory of random forests lags far behind their applications. In this paper, we propose a novel random forests classification algorithm based on cooperative game theory. The Banzhaf power index is employed to evaluate the power of each feature by traversing possible feature coalitions. Hence, we call the proposed algorithm Banzhaf random forests (BRFs). Unlike the previously used information gain ratio, which only measures the power of each feature for classification and pays less attention to the intrinsic structure of the feature variables, the Banzhaf power index can measure the importance of each feature by computing the dependency among the group of features. More importantly, we have proved the consistency of BRFs, which narrows the gap between the theory and applications of random forests. Extensive experiments on several UCI benchmark data sets and three real world applications show that BRFs perform significantly better than existing consistent random forests on classification accuracy, and better than or at least comparable with Breiman’s random forests, support vector machines (SVMs) and k-nearest neighbors (KNNs) classifiers. Highlights • A novel random classification forests algorithm, called Banzhaf random forests (BRFs), is proposed. • The Banzhaf power index is employed to evaluate the power of each feature by traversing possible feature coalitions. • The consistency of BRFs is proved. [ABSTRACT FROM AUTHOR]

The neutron field measurement was performed in the Experimental Advanced Superconducting Tokamak (EAST) experimental hall using a Bonner sphere spectrometer (BSS) based on a 3 He thermal neutron counter. The measured spectra and the corresponding integrated neutron fluence and dose values deduced from the spectra at two exposed positions were compared to the calculated results obtained by a general Monte Carlo code MCNP5, and good agreements were found. The applicability of a homemade dose survey meter installed at EAST was also verified with the comparison of the ambient dose equivalent H*(10) values measured by the meter and BSS. [ABSTRACT FROM AUTHOR]

The influence of environmental light sources affects the colour cast in natural images. In computer vision, biased colours have a significant influence on object recognition and classification. Illuminant estimation aims to eliminate these effects and obtain the image in canonical white light. In this study, the authors propose a deep non‐negative matrix factorisation (DeepNMF) method to estimate the illuminant of colour‐biased images. DeepNMF deeply factorises the input matrix into multiple layers, separating the image into patches and reshaping each channel of the patch as an [R,G,B] matrix. Based on the diagonal model, they assume that the final layer is the estimated illuminant of each patch. Mean pooling is then used to estimate the illuminant of the overall image. The angular error is used as a metric to test the authors' method on three commonly used colour constancy datasets. The results show that the proposed method is comparable to state‐of‐the‐art methods, although it is simpler to implement. As the proposed method uses a single image as input, it does not require a learning process. [ABSTRACT FROM AUTHOR]

This paper presents a general framework for short text classification by learning vector representations of both words and hidden topics together. We refer to a large-scale external data collection named ”corpus” which is topic consistent with short texts to be classified and then use the corpus to build topic model with Latent Dirichlet Allocation (LDA). For all the texts of the corpus and short texts, topics of words are viewed as new words and integrated into texts for data enriching. On the enriched corpus, we can learn vector representations of both words and topics. In this way, feature representations of short texts can be performed based on vectors of both words and topics for training and classification. On an open short text classification data set, learning vectors of both words and topics can significantly help reduce the classification error comparing with learning only word vectors. We also compared the proposed classification method with various baselines and experimental results justified the effectiveness of our word/topic vector representations. [ABSTRACT FROM AUTHOR]

Objective: To explore the role of platelet-derived growth factor-B (PDGF-B)/platelet-derived growth factor receptor-β (PDGFR-β) signaling in chronic atrial fibrillation (AF). Methods: Thirty-nine AF patients and 33 patients with sinus rhythm (SR) were enrolled. Twenty canines were randomized into 5 groups: control, sham and AF lasting 1, 2 or 4 weeks. The AF canine models were made by rapid atrial pacing. Rat atrial fibroblasts were treated with PDGF-BB or PDGF-BB + PDGFR inhibitor AG1295, respectively. Gene expression in the right atrial appendage of patients, the left atrium of canines and rat atrial fibroblasts was measured by quantitative real-time PCR and Western blot, respectively. The degree of atrial fibrosis was evaluated by Masson trichrome staining. Results: The degree of atrial fibrosis and the expression of PDGF-B, PDGFR-β and collagen type I (COL1) in AF patients significantly increased compared to patients with SR. The degree of atrial fibrosis and the expression of PDGF-B and COL1 in canines increased progressively with the increased duration of AF. The expression of PDGFR-β increased progressively 2 weeks after AF. PDGF-BB promoted the proliferation and COL1 secretion of rat atrial fibroblasts. AG1295 attenuated these effects. Conclusions: Our study suggests that PDGF-B/PDGFR-β signaling, which promotes the proliferation and COL1 secretion of atrial fibroblasts, is an important contributor to atrial fibrosis in AF and may represent a novel target for the intervention of AF. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a novel framework for text identification and recognition, called tensor representation learning based image patch analysis (TRL-IPA). Unlike most of previous text identification approaches, which can only be applied to binarized images, TRL-IPA can be directly applied to gray level and color images. TRL-IPA is built on a general formulation of the convergent tensor representation learning (CTRL) algorithms. In the implementation of TRL-IPA, image patches are represented in the form of tensors, while low dimensional representations of these tensors are learned via a CTRL algorithm. To identify text regions in new coming document images, a random forest classifier is trained in the learned tensor subspace. Moreover, the TRL-IPA framework can be straightforwardly applied to recognition problems, such as handwritten digits recognition. We conducted extensive experiments on ancient Chinese, Arabic and Cyrillic document images, to evaluate TRL-IPA on text identification tasks. Experimental results demonstrate its effectiveness and robustness. In addition, recognition results on images of handwritten digits show its advantage over state-of-the-art vector and tensor representation based approaches. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes a novel feature extraction method based on ensemble learning. Using the error-correcting output codes (ECOC) to design binary classifiers (dichotomizers) for separating subsets of classes, the outputs of the dichotomizers are linear or nonlinear features that provide powerful separability in a new space. In this space, the vector quantization based meta classifier can be viewed as an ECOC decoder, where each learned prototype of a class can be seen as a codeword of the class in the new representation space. We conducted extensive experiments on 16 multi-class data sets from the UCI machine learning repository. The results demonstrate the superiority of the proposed method over both existing ECOC approaches and classic feature extraction approaches. In particular, the decoding strategy using a meta classifier is shown to be more computationally efficient than the linear loss-weighted decoding in state-of-the-art ECOC methods. [Copyright &y& Elsevier]

The ECOC technique is a powerful tool to learn and combine multiple binary learners for multi-class classification. It generally involves three steps: coding, dichotomizers learning, and decoding. In previous ECOC methods, the coding step and the dichotomizers learning step are usually performed independently. This simplifies the learning problem but may lead to unsatisfactory decoding results. To solve this problem, we propose a novel model for learning the ECOC matrix and dichotomizers jointly from data. We formulate the model as a nonlinear programming problem and develop an efficient alternating minimization algorithm to solve it. Specifically, for fixed ECOC matrix, our model is decomposed into a group of mutually independent quadratic programming problems; while for fixed dichotomizers, it is a difference of convex functions problem and can be easily solved using the concave--convex procedure algorithm. Our experimental results on ten data sets from the UCI machine learning repository demonstrated the advantage of our model over state-of-the-art ECOC methods. [ABSTRACT FROM AUTHOR]

The quantum convolutional neural networks (QCNNs) are emerging as a promising solution for image classification problems on near-term quantum devices. While QCNNs have shown encouraging results in binary classification tasks, their effectiveness in more complex multiclass classification tasks remains to be fully understood. In this study, we propose three distinct QCNN architectures, each inspired by the configuration of two-qubit entangling blocks available in quantum hardware. These QCNNs are designed based on different sliding modes of quantum filters. We investigate the impact of quantum filter structure, filter arrangement and parameter sharing among filters on the performance of QCNNs in multiclass classification. Our findings indicate that the specific structure of the quantum filter significantly affects the models' performance. Moreover, we observed that unsharring parameters and more complex filter arrangements can significantly enhance the performance of QCNNs. These results contribute to the development of powerful quantum classifiers for multiclass image classification. [ABSTRACT FROM AUTHOR]

Background. There is still no clear understanding of the pathogenesis of atrial fibrillation (AF). For this purpose, we used integrated analysis to uncover immune infiltration characteristics and investigated their relationship with competing endogenous RNA (ceRNA) network in AF. Methods. Three AF mRNA data sets (GSE14975, GSE79768, and GSE41177) were integrated using the SVA method from Gene Expression Omnibus (GEO). Together with AF circRNA data set (GSE129409) and miRNA data set (GSE70887) from GEO database, we built a ceRNA network. Then hub genes were screened by the Cytoscape plug-in cytoHubba from a protein-protein interaction (PPI) network. As well, CIBERSORT was employed to investigate immune infiltration, followed by Pearson correlation coefficients to unravel the correlation between AF-related infiltrating immune cells and hub genes. Ulteriorly, circRNA-miRNA-mRNA regulatory axises that could be immunologically related to AF were obtained. Results. Ten hub genes were identified from the constructing PPI network. The immune infiltration analysis revealed that the number of monocytes and neutrophils was higher, as well as the number of dendritic cells activated and T cells regulatory (Tregs) was lower in AF. Seven hub genes (C5AR1, CXCR4, HCK, LAPTM5, MPEG1, TLR8, and TNFSF13B) were associated with those 4 immune cells (P < 0.05). We found that the circ_0005299–miR-1246–C5AR1 and circRNA_0079284-miR-623-HCK/CXCR4 regulatory axises may be associated with the immune mechanism of AF. Conclusion. The findings of our study provide insights into immuno-related ceRNA networks as potential molecular regulators of AF progression. [ABSTRACT FROM AUTHOR]

By contrast with the widely reported traditional two mirror-based Herriott cell, a three mirror-based dense pattern gas cell was proposed, of which the modeling and design were proven to be effective through a comparison between the simulated spot pattern and effective path length and those of the experimental results. A mechanical structure was designed to adjust the position/angle of the three mirrors for aligning the optical path. The experimentally measured reflection number was 60, resulting in an optical path length of ~11 m, which agrees well with the theoretical value of 10.95 m. Combined with a near-infrared laser with a center wavenumber located at an acetylene (C2H2) absorption line of 6521.2 cm−1, a C2H2 sensor system was established to verify the feasibility of the three mirror-based gas cell. Assisted by a data acquisition (DAQ) card, a LabVIEW platform was developed to generate the drive signal of the laser and acquire the second harmonic (2f) signal from the output of the detector. Through Allan variance analysis, the limit of detection (LoD) of the sensor system is 4.36 ppm at an average time of 0.5 s; as the average time exceeds 10 s, the LoD is <1 ppm. The proposed model and design of the three mirror-based gas cell can be used to realize similar gas cells with different absorption path lengths for gas detection based on infrared absorption spectroscopy. [ABSTRACT FROM AUTHOR]

Objective. To investigate the characteristics of dynamic electrocardiogram and their clinical implications in elderly patients with nonvalvular atrial fibrillation combined with long R-R intervals. Methods. Elderly patients diagnosed with nonvalvular atrial fibrillation who were admitted as an inpatient or attended the outpatient department from January 2015 to January 2020 were selected. Patients were divided into two groups based on the presence of a long R-R interval. The characteristics and therapeutic significance of dynamic electrocardiogram between the two groups were compared. Results. A total of 532 patients were included in our analyses. Of these, 399 patients were in the long R-R interval group and 133 in the nonlong R-R interval group. In 399 patients, there were 48,840 long R-R intervals manifested within 24 hours. The average, slowest, and fastest ventricular rates during sleep time were higher than those in nonsleep time, while the number of long R-R intervals in sleep time was significantly smaller than that in nonsleep time (P < 0.05). Clinical parameters including dizziness/syncope, cerebral infarction, ST-segment changes, platelet count, average hematocrit, prothrombin time (PT), left ventricular systolic function, end-diastolic diameter, pulmonary artery pressure, and left ventricular ejection fraction were comparable between the groups (P > 0.05). When compared with the nonlong R-R interval group, the level of C-reactive protein was slightly lower in the long R-R interval group (P < 0.05). In addition, the long R-R interval group had a higher incidence of atrial premature beats but a lower incidence of ventricular premature beats. Furthermore, the probability of long R-R interval combined with paroxysmal atrial tachycardia, transient ventricular arrest, second-degree atrioventricular block, and complete or incomplete right bundle branch block was higher than that of nonlong R-R interval (P < 0.05). In patients with long R-R interval >3 s, the risk of having second-degree atrioventricular block and complete or incomplete right bundle branch block was significantly lower, while the risk of having transient ventricular arrest was higher when compared to patients with long R-R intervals of 2-3 s (P < 0.05 P). Conclusions. Long R-R interval is a common electrocardiographic phenomenon among the elderly with nonvalvular atrial fibrillation. The long R-R interval mostly occurs in nonsleeping time. The average ventricular rate, slowest ventricular rate, and fastest ventricular rate of sleep time are higher than nonsleeping time. Analysis of the characteristics of the dynamic electrocardiogram of these patients may shed light on the mechanisms for long R-R intervals, including the likelihood of concealed conduction and physiological interference in the atrioventricular node, overspeed inhibition, increased vagus nerve tension, or pathological atrioventricular block. [ABSTRACT FROM AUTHOR]

Mechanochemical synthesis is a technology formed in the 1950s. By applying mechanical energy, mechanochemical synthesis stimulates chemical reactions, causing structural and phase changes and enabling difficult or otherwise impossible reactions to occur. Mechanochemical reactions are relatively safe, clean and efficient. At present, the synthesis of oxide ceramic materials by mechanochemical methods has attracted widespread attention. In the production of oxide ceramic materials, the characteristics of ceramic powder have a certain influence on the molding, sintering, microstructure, and mechanical properties of subsequent ceramics. In this paper, the research progress of mechanical chemistry in the synthesis of nanostructures and composite oxide ceramic powders is reviewed. The effects of process parameters such as ball milling time, speed, atmosphere, grinding media, additives, and ball‐to‐powder ratio on reaction kinetics, phase formation, and powder properties are summarized. In addition, challenges and opportunities for ceramic powder synthesis are also discussed. [ABSTRACT FROM AUTHOR]

Since Pearson developed principal component analysis (PCA) in 1901, feature learning (or called representation learning) has been studied for more than 100 years. During this period, many "shallow" feature learning methods have been proposed based on various learning criteria and techniques, until the popular deep learning research in recent years. In this advanced review, we describe the historical profile of the shallow feature learning research and introduce the important developments of the deep learning models. Particularly, we survey the deep architectures with benefits from the optimization of their width and depth, as these models have achieved new records in many applications, such as image classification and object detection. Finally, several interesting directions of deep learning are presented and briefly discussed. This article is categorized under: Technologies > ClassificationTechnologies > Machine LearningTechnologies > Computational Intelligence Relationship between data representation learning algorithms. Please refer to the text for the full names of the algorithms. [ABSTRACT FROM AUTHOR]

Ensemble learning, online learning and deep learning are very effective and versatile in a wide spectrum of problem domains, such as feature extraction, multi-class classification and retrieval. In this paper, combining the ideas of ensemble learning, online learning and deep learning, we propose a novel deep learning method called deep error-correcting output codes (DeepECOCs). DeepECOCs are composed of multiple layers of the ECOC module, which combines several incremental support vector machines (incremental SVMs) as base classifiers. In this novel deep architecture, each ECOC module can be considered as two successive layers of the network, while the incremental SVMs can be viewed as weighted links between two successive layers. In the pre-training procedure, supervisory information, i.e., class labels, can be used during the network initialization. The incremental SVMs lead this procedure to be very efficient, especially for large-scale applications. We have conducted extensive experiments to compare DeepECOCs with traditional ECOC, feature learning and deep learning algorithms. The results demonstrate that DeepECOCs perform, not only better than existing ECOC and feature learning algorithms, but also related to deep learning ones in most cases. [ABSTRACT FROM AUTHOR]

Mitophagy is an autophagic response and plays essential roles in survival, development, and homeostasis of cells. It has been reported that mitophagic dysfunction is involved in several cardiovascular diseases. However, the effect of mitophagy on atrial fibrillation (AF) is still unknown. Therefore, we investigated the exact role of mitophagy in human chronic AF. Western blot was used to detect the protein abundance. The mitochondrial morphology and structure were observed by transmission electron microscopy. Immunofluorescent stainings were performed to analyze colocalization of mitochondria with autophagosomes or lysosomes. Totally, 43 patients with valvular heart disease undergoing cardiac surgery were selected, including 21 patients with chronic AF. Comparing with the sinus rhythm (SR) group, we found the size and number of mitochondria in atrial myocytes of patients with AF increased significantly. In addition, expression of LC3B II and LC3B II/LC3B I ratio was significantly decreased in the AF group. Moreover, the expression of p62 was markedly elevated in the AF group compared with that in the SR group. The results of immunofluorescence staining and western blot showed an enhanced expression of Cox IV in the AF group. Dual immunofluorescent stainings revealed that mitophagy defect in atrial myocytes of patients with AF resulted from dysfunction in the process of delivery of mitochondria into autophagosomes. For the first time, impaired mitophagy, during the phagocytosis of mitochondria, is associated with human chronic AF. Mitophagy could be a potential therapeutic target for AF. [ABSTRACT FROM AUTHOR]

Radiation rays, especially gamma rays, are produced during nuclear fusion plasma discharge. To obtain the gamma rays around the Experimental Advanced Superconducting Tokamak (EAST) during nuclear fusion plasma discharges, Ar-based gamma ray detectors have been adopted in this article, and to fully monitor the gamma ray dose rates inside the EAST hall, three gamma ray detectors have been set at three selected radiation monitoring sites. Due to the long distances between the monitoring personal computer (PC) and each radiation monitoring site, the monitoring system based on the RS-485 differential communication mode has been designed and set up. With each plasma discharge, the gamma ray radiation field undoubtedly appears as a pulsed radiation field in the EAST hall. Based on the gamma ray dose rates obtained by the monitoring system, the pulsed radiation field of gamma ray dose rates has been carefully studied during one lower hybrid current drive (LHCD)-based plasma discharge. Finally, the exponential function model of the gamma ray dose rates during one plasma discharge in the EAST hall was well obtained and presented. [ABSTRACT FROM AUTHOR]

A neutron activation system (NAS) has been developed on the Experimental Advanced Superconducting Tokamak (EAST) for the investigation of time-integrated neutron yield. It is a robust neutron diagnostic with high precision and a wide dynamic range. Some high purity materials with a proper nuclear reaction energy threshold and cross section are tested as neutron detectors in the NAS, and they are delivered to the tokamak device by a pneumatic transfer system. The length of the pneumatic pipeline is about 50 m, and the transfer time ranges from 10 s to 20 s. The decay gamma rays of the activated samples are measured with a high-purity germanium spectrometer, and its detection efficiencies are 6.9% at 336 keV and 1.7% at 1779 keV, respectively. Indium and silicon samples with a reaction threshold of 0.3 MeV and 4.0 MeV, respectively, were successfully implemented in the diagnosis of DD and DT neutron yield in the EAST. The neutron flux of the NAS was compared with the count of the fission chamber in the EAST neutral beam injection experiment, and the DD neutron yield evaluated by the NAS ranges from 1.9 × 1012 n/shot to 5.9 × 1014 n/shot during recent experimental campaign. [ABSTRACT FROM AUTHOR]

Two high performance single crystal diamond (SCD) radiation detectors for neutrons and gamma ray have been fabricated. One was coated with a 5 μm 6LiF (95% enriched in 6Li) layer in order to detect the neutrons and the other one is without any conversion layer for comparison. The detectors are irradiated with the 241Am-Be and 252Cf neutron sources and 152Eu gamma source to test their performance. Under neutron sources, the neutron signal peak was clearly observed in the detector with the 6LiF conversion layer. Meanwhile, both detectors are responsive to the gamma rays at the low energy. The 152Eu gamma source was specially used to verify the gamma ray detection ability of the fabricated SCD detectors. The results show that the detector's count rate and the gamma peak center position in the PHS depend on the incident gamma spectra critically. Combined, these results demonstrate that the design and fabrication process of the detectors are feasible. [ABSTRACT FROM AUTHOR]

The structural health monitoring system (SHM) of long-span bridges inevitably produces low-quality data. It is important to evaluate the data quality and screen out normal data. Most existing studies on data anomaly detection have focused on abnormal data with abnormal waveforms in time domain. Usually there are pseudo-normal data in the SHM system, which seems normal in time domain. However, the pseudo-normal data of bridge dynamic responses may cause wrong dynamic characteristics identification. In this study, the existing data anomaly detection was further expanded to data quality evaluation, and both obvious abnormal and pseudo-normal data are evaluated. A general data quality evaluation framework for dynamic response monitoring of long-span bridge structures was proposed. The time–frequency characteristics of monitoring data were obtained by continuous wavelet transform, and the data quality was classified and evaluated by constructing and training a convolutional neural network (CNN). The proposed framework was verified by using acceleration monitoring data of a cable-stayed bridge. The results reveal that this framework solves the problem that CNN cannot detect pseudo-normal data through time-domain characteristics, and improves the accuracy of data quality evaluation by using time–frequency information of the monitoring data. The acceleration monitoring data of a suspension bridge and a single pylon cable-stayed bridge were used to demonstrate the feasibility of the cross-object application of the proposed framework. The evaluation accuracy for the acceleration data quality of the suspension bridge exceeded 96%. After the reinforced training of a small number of pseudo-normal data samples, the evaluation accuracy for the acceleration data quality of the cables of the cable-stayed bridge reached 96.8%. The framework shows a good generalization capacity and robustness in cross-object applications. [ABSTRACT FROM AUTHOR]

Quantum neural network (QNN) is one of the promising directions where the near-term noisy intermediate-scale quantum (NISQ) devices could find advantageous applications against classical resources. Recurrent neural networks are the most fundamental networks for sequential learning, but up to now there is still a lack of canonical model of quantum recurrent neural network (QRNN), which certainly restricts the research in the field of quantum deep learning. In the present work, we propose a new kind of QRNN which would be a good candidate as the canonical QRNN model, where, the quantum recurrent blocks (QRBs) are constructed in the hardware-efficient way, and the QRNN is built by stacking the QRBs in a staggered way that can greatly reduce the algorithm's requirement with regard to the coherent time of quantum devices. That is, our QRNN is much more accessible on NISQ devices. Furthermore, the performance of the present QRNN model is verified concretely using three different kinds of classical sequential data, i.e., meteorological indicators, stock price, and text categorization. The numerical experiments show that our QRNN achieves much better performance in prediction (classification) accuracy against the classical RNN and state-of-the-art QNN models for sequential learning, and can predict the changing details of temporal sequence data. The practical circuit structure and superior performance indicate that the present QRNN is a promising learning model to find quantum advantageous applications in the near term. [ABSTRACT FROM AUTHOR]

Radiation particles and rays will be produced during each nuclear fusion plasma discharge, especially neutrons which will be largely emitted out with high intensity and energy under the neutral beam injection (NBI) discharge mode. To well obtain the neutron dose rates during the nuclear fusion plasma discharge, 3He-based neutron detector is adopted in this paper, and to fully obtain the neutron dose rates in the Experimental Advanced Superconducting Tokamak (EAST) hall, three neutron detectors have been set at three selected monitoring sites with a long distance to the monitoring personal computer (PC), and the monitoring system, based on the RS-485 differential communication mode, was designed and setup. Along with each nuclear fusion plasma discharge, the neutron radiation field should appear as a pulsed radiation field. Based on the neutron dose rates, saved by the monitoring system, the neutron radiation dose rates were studied during one NBI-based nuclear fusion plasma discharge. Finally, the Gaussian function model of neutron dose rates during one nuclear fusion plasma discharge was obtained and presented. [ABSTRACT FROM AUTHOR]

Atrial fibrosis is a crucial contributor to initiation and perpetuation of atrial fibrillation (AF). This study aimed to identify a circRNA-miRNA-mRNA competitive endogenous RNA (ceRNA) regulatory network related to atrial fibrosis in AF, especially to validate hsa_circ_0000672/hsa_miR-516a-5p/TRAF6 ceRNA axis in AF preliminarily. The circRNA-miRNA-mRNA ceRNA network associated with AF fibrosis was constructed using bioinformatic tools and literature reviews. Left atrium (LA) low voltage was used to represent LA fibrosis by using LA voltage matrix mapping. Ten controls with sinus rhythm (SR), and 20 patients with persistent AF including 12 patients with LA low voltage and 8 patients with LA normal voltage were enrolled in this study. The ceRNA regulatory network associated with atrial fibrosis was successfully constructed, which included up-regulated hsa_circ_0000672 and hsa_circ_0003916, down-regulated miR-516a-5p and five up-regulated hub genes (KRAS, SMAD2, TRAF6, MAPK11 and SMURF1). In addition, according to the results of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, these hub genes were clustered in TGF-beta and MAPK signaling pathway. In the patients with persistent AF, hsa_circ_0000672 expression in peripheral blood monocytes was significantly higher than those in controls with SR by quantitative real-time polymerase chain reaction (p-value < 0.001). Furthermore, hsa_circ_0000672 expression was higher in peripheral blood monocytes of persistent AF patients with LA low voltage than those with LA normal voltage (p-value = 0.002). The dual-luciferase activity assay confirmed that hsa_circ_0000672 exerted biological functions as a sponge of miR-516a-5p to regulate expression of its target gene TRAF6. Hsa_circ_0000672 expression in peripheral blood monocytes may be associated with atrial fibrosis. The hsa_circ_0000672 may be involved in atrial fibrosis by indirectly regulating TRAF6 as a ceRNA by sponging miR-516a-5p. [ABSTRACT FROM AUTHOR]

For the safe operation and maintenance of neutral beam injector (NBI), specific activity and shutdown contact dose rate of the sample material SS316 are estimated around the experimental advanced superconducting tokamak (EAST) neutral beam port. Firstly, the neutron emission intensity is calculated by TRANSP code while the neutral beam is co-injected to EAST. Secondly, the neutron activation and shutdown contact dose rates for the neutral beam sample materials SS316 are derived by the Monte Carlo code MCNP and the inventory code FISPACT-2007. The simulations indicate that the primary radioactive nuclides of SS316 are 58 Co and 54 Mn. The peak contact dose rate is 8.52 × 10 −6 Sv/h after EAST shutdown one second. That is under the International Thermonuclear Experimental Reactor (ITER) design values 1 × 10 −5 Sv/h. [ABSTRACT FROM AUTHOR]

With the rapidly increasing market demand of lithium-ion batteries (LIBs), safety has become the main focus and challenge in realizing high-energy and high-safety LIBs. In this paper, a light-weight composite current collector, which is prepared by sandwiching a polyethylene terephthalate (PET) film between two ultrathin aluminum (Al) layers, is designed to improve the safety performances of the LIBs. The PET film is selected as the supporting film due to its low density, superior mechanical properties, excellent thermal stability, and remarkable flame resistance. The thermal and mechanical stabilities of the PET-Al based electrode systems, as well as the electrochemical performances of the corresponding LIBs, are evaluated. Nail penetration test further is carried out to investigate the internal short-circuit behaviors of the batteries, which is the most extreme and hazardous condition and may cause thermal runaway and catastrophic accidents. The results well demonstrate that the proposed PET-Al current collector can slow down the temperature rise of the entire battery during the nail penetration, and effectively achieve the aim of improving the reliability performances of LIBs. • A composite-based current collector is fabricated by sandwiching a PET film between two ultrathin Al layers. • The PET-Al film possesses good thermal and mechanical stabilities, as well as good electrochemical properties. • The lighter weight of PET-Al current collector can promote the energy density of the LIB. • The PET-Al-based LIB passes the nail penetration test with no flame evidence. • The PET-Al current collector can effectively improve the safety performance of the LIB. [ABSTRACT FROM AUTHOR]

Currently, high dimensional data processing confronts two main difficulties: inefficient similarity measure and high computational complexity in both time and memory space. Common methods to deal with these two difficulties are based on dimensionality reduction and feature selection. In this paper, we present a different way to solve high dimensional data problems by combining the ideas of Random Forests and Anchor Graph semi-supervised learning. We randomly select a subset of features and use the Anchor Graph method to construct a graph. This process is repeated many times to obtain multiple graphs, a process which can be implemented in parallel to ensure runtime efficiency. Then the multiple graphs vote to determine the labels for the unlabeled data. We argue that the randomness can be viewed as a kind of regularization. We evaluate the proposed method on eight real-world data sets by comparing it with two traditional graph-based methods and one state-of-the-art semi-supervised learning method based on Anchor Graph to show its effectiveness. We also apply the proposed method to the subject of face recognition. [ABSTRACT FROM AUTHOR]

A significant reduction in neutron yield in the plasma core has been observed in EAST neutral beam injection into plasmas with a low-edge safe factor of q 95 = 3.5 and a n = 2 resonant magnetic perturbation (RMP) during a sawtooth crash. Harmonics of the m/n = 1/1 mode (m and n being the poloidal and toroidal mode numbers) develop simultaneously. A sawtooth crash is triggered when the current of the RMP exceeds a critical value; saturated fishbone phase occurs with small RMP currents. Soft x-ray (SXR) tomographic patterns obtained during sawtooth crashes have annular hot-ring shapes outside the q = 1 surface that are similar to those of double tearing modes triggered by off-axis sawtooth crashes in TFTR reversed magnetic shear plasma. M3D nonlinear simulations confirmed the initial penetration of the n = 2 RMP in the plasma core and further demonstrated a complete sawtooth crash with the appearance of a right annular hot ring near the q = 1 surface. Reconstruction of the trajectory of SXR signals before the crash shows chaotic behavior. Moreover, a high-frequency mode was seen to appear just before the sawtooth crash, accompanied by an edge geodesic acoustic mode. This edge mode is free in the saturated fishbone phase. The role of high-frequency mode in the sawtooth crash remains unclear. [ABSTRACT FROM AUTHOR]

Face image-sketch synthesis is widely applied in law enforcement and digital entertainment fields. Despite the extensive progression in face image-sketch synthesis, there are few methods focusing on generating a color face image from a sketch. The existing methods pay less attention to learning the illumination or highlight distribution on the face region. However, the illumination is the key factor that makes the generated color face image looks vivid and realistic. Moreover, existing methods tend to employ some image preprocessing technologies and facial region patching approaches to generate high-quality face images, which results in the high complexity and memory consumption in practice. In this paper, we propose a novel end-to-end generative adversarial fusion model, called GAF, which fuses two U-Net generators and a discriminator by jointly learning the content and adversarial loss functions. In particular, we propose a parametric tanh activation function to learn and control illumination highlight distribution over faces, which is integrated between the two U-Net generators by an illumination distribution layer. Additionally, we fuse the attention mechanism into the second U-Net generator of GAF to keep the identity consistency and refine the generated facial details. The qualitative and quantitative experiments on the public benchmark datasets show that the proposed GAF has better performance than existing image-sketch synthesis methods in synthesized face image quality (FSIM) and face recognition accuracy (NLDA). Meanwhile, the good generalization ability of GAF has also been verified. To further demonstrate the reliability and authenticity of face images generated using GAF, we use the generated face image to attack the well-known face recognition system. The result shows that the face images generated by GAF can maintain identity consistency and well maintain everyone's unique facial characteristics, which can be further used in the benchmark of facial spoofing. Moreover, the experiments are implemented to verify the effectiveness and rationality of the proposed parametric tanh activation function and attention mechanism in GAF. [ABSTRACT FROM AUTHOR]

Geopolymer is a porous inorganic material with a three-dimensional mesh structure, good mechanical properties, a simple preparation process (no sintering) and a low economic cost, and it is environmentally friendly. Geopolymer concrete has been widely used in the construction field, and many other studies have revealed that geopolymer will become one of the most promising inorganic materials with unique structure and properties. This paper provides a review of the development and current status of geopolymers and briefly explains the effects of material proportioning, experimental factors and activators on geopolymer performance. Because of the advantages of high specific surface area and high porosity, geopolymers could be used as adsorbent materials. This paper summarizes the research progresses of the adsorption of metal cations, anions, dyes, and gases by geopolymers, which emphasizes the geopolymer membranes in adsorption, and discusses the challenges and opportunities for the development of more efficient, sustainable and practical adsorption protocols. [ABSTRACT FROM AUTHOR]

Bipolar membranes, a new type of composite ion exchange membrane, contain an anion exchange layer, a cation exchange layer and an interface layer. The interface layer or junction is the connection between the anion and cation exchange layers. Water is dissociated into protons and hydroxide ions at the junction, which provides solutions to many challenges in the chemical, environmental and energy fields. By combining bipolar membranes with electrodialysis technology, acids and bases could be produced with low cost and high efficiency. The interface layer or junction of bipolar membranes (BPMs) is the connection between the anion and cation exchange layers, which the membrane and interface layer modification are vital for improving the performance of BPMs. This paper reviews the effect of modification of a bipolar membrane interface layer on water dissociation efficiency and voltage across the membrane, which divides into three aspects: organic materials, inorganic materials and newly designed materials with multiple components. The structure of the interface layer is also introduced on the performance of bipolar membranes. In addition, the remainder of this review discusses the challenges and opportunities for the development of more efficient, sustainable and practical bipolar membranes. [ABSTRACT FROM AUTHOR]

In this study, a series of Sm 2 O 3 micron plates/B 4 C/HDPE composites composed of synthesized Sm 2 O 3 fillers (Sm 2 O 3 micron plates) are prepared for shielding neutron and gamma radiation. The influence of micromorphology of Sm 2 O 3 fillers on neutron and gamma radiation shielding properties of composites is investigated in detail. The XRD pattern reveals that the phase of synthetic Sm 2 O 3 is cubic crystal systems, body-centered cubic lattices, and its space group is Ia 3 ‾ (206). SEM images and BET analyses reveal that the micromorphology of synthesized Sm 2 O 3 is micron plates. The BET-specific surface area of the Sm 2 O 3 fillers is increased with addition of urea content. The differential scanning calorimetry (DSC) curves reveal that Sm 2 O 3 fillers increase the melting temperature of the composites, which is up to 138.6 °C. The thermogravimetric analysis (TGA) results reveal that the initial thermal degradation temperatures of the composites are all above 440 °C. The neutron and gamma radiation shielding tests show that Sm 2 O 3 fillers with high BET-specific surface area (8.20 m2/g) and uniform size improve the neutron and gamma shielding rate of composites. A superior composite containing 10 wt% Sm 2 O 3 (R = 1:25, R value represents the molar ratio of rare earth elements to urea)/20 wt% B 4 C/70 wt% HDPE has a neutron radiation shielding rate of 98.7% with a thickness of 15 cm under the 252Cf neutron source and a gamma radiation shielding rate of 72.1% with a thickness of 15 cm under 137Cs gamma source. And these lead-free and environment-friendly composites can be widely used in the neutron and gamma complex radiation fields. [Display omitted] [ABSTRACT FROM AUTHOR]

• AGCNs use Gabor filters to manipulate convolutional kernels. • We have proved that AGCNs can learn the invariant information from images. • The parameters in the Gabor filters can be updated during the model training. • We demonstrate the robustness of AGCNs against adversarial attacks. [Display omitted] Despite the great breakthroughs that deep convolutional neural networks (DCNNs) have achieved on image representation learning in recent years, they lack the ability to extract invariant information from images. On the other hand, several traditional feature extractors like Gabor filters are widely used for invariant information learning from images. In this paper, we propose a new class of DCNNs named adaptive Gabor convolutional networks (AGCNs). In the AGCNs, the convolutional kernels are adaptively multiplied by Gabor filters to construct the Gabor convolutional filters (GCFs), while the parameters in the Gabor functions (i.e., scale and orientation) are learned alongside those in the convolutional kernels. In addition, the GCFs can be regenerated after updating the Gabor filters and convolutional kernels. We evaluate the performance of the proposed AGCNs on image classification using five benchmark image datasets, i.e., MNIST and its rotated version, SVHN, CIFAR-10, CINIC-10, and DogsVSCats. Experimental results show that the AGCNs are robust to spatial transformations and have achieved higher accuracy compared with the DCNNs and other state-of-the-art deep networks. Moreover, the GCFs can be easily embedded into any classical DCNN models (e.g., ResNet) and require fewer parameters than the corresponding DCNNs. [ABSTRACT FROM AUTHOR]

• Two shields that each contain a collimator and simultaneously shield neutrons and gamma-rays are designed for neutron and gamma-ray diagnostics. • Monte Carlo simulations are used to determine the optimal materials and thicknesses of the shielding layers. • Replaceable collimation aperture adjustment modules are adopted, and the effects of the structure used are specifically analyzed. • For incident particles of different energies, the efficiency of the collimator is estimated. • The particle fluxes in the sensitive energy ranges of the detectors are used as the basis for evaluation. New compact radiation shields have been designed to reduce the scattered neutrons and background gamma-rays for neutron and gamma-ray diagnostics in the EAST hall. The design focused on optimizing radiation shielding performance versus volume and weight to fit the small amount of available space outside the tokamak device. Based on the Monte Carlo method and using the Geant4 tool kit, three-dimensional models of the shield were created for material selection and structural design optimization. The 10-cm and 15-cm polyethylene doped with 12 wt.% B 4 C, and 10-cm cast lead were finally selected for the main body of the shield. In addition, the shape and structure of the collimator in the front half of the shield are optimized, and a replaceable aperture adjuster module design has been adopted for the collimator. For incident particles with different energies, the collimation efficiency of the collimator has also been calculated. [ABSTRACT FROM AUTHOR]

[Display omitted] • The surface chemical composition of β-Ga 2 O 3 films is discussed in depth. • The component of O 1s with the binding energy around 531.8 eV is attributed to surface lattice O atoms. • The defects inside the β-Ga 2 O 3 films were analyzed by HRTEM. β gallium oxide (β-Ga 2 O 3) is an ultrawide bandgap semiconductor with great potential for solar-blind ultraviolet detection, power devices, and gas sensing. However, the understanding of the surface properties of β-Ga 2 O 3 remains insufficient. Here, epitaxial growth of β-Ga 2 O 3 films was carried by metal–organic chemical vapor deposition (MOCVD) on sapphire substrates. The surface chemical composition, surface layer crystalline properties, and surface morphology of the β-Ga 2 O 3 films were discussed and analyzed in detail. By comparing the atomic ratios of O/Ga, the surface morphology, and the crystalline properties of the films, we attribute the component of O 1s with the binding energy around 531.8 eV to surface lattice O atoms. Moreover, the one-dimensional defects inside the surface layers and the near-interface regions of the films were observed at atomic scale by high resolution transmission electron microscopy (HRTEM). By comparing the lattice fringes of different facets, the geometric phase uniformity, and the distribution of strain, we found that the crystalline quality of the surface layers is much higher than the near-interface regions. [ABSTRACT FROM AUTHOR]

Background: In recent years, there has been a large amount of studies about the efficacy and safety of vernakalant or RSD1235, an antiarrhythmic agent, in treating the atrial fibrillation (AF). This study was designed to assess the efficacy and safety of vernakalant in the treatment of AF. Results: A total of 5 randomized controlled trials (RCTs) (n= 1153) met our inclusion criteria. Vernakalant was superior in achieving sinus rhythm (SR) for AF comparing to placebo or alternative anti-arrhythmic agents (relative risk [RR] = 11.56, 95% Confidence Interval [CI] = 7.12 - 18.75). There was no heterogeneity among the trials (X² =0.59, P = 0.96). In analysing the adverse effects of cardiac origin, there was no significant difference between the two groups (RR= 0.90, 95% CI = 0.52 - 1.57). Methods: The Cochrane library, Pubmed NCBI, EMBASE and MEDLINE were systematically searched to identify all interventional trials of vernakalant with placebo or other antiarrhythmic drug in converting AF to SR. The primary outcome was rate of converting to SR, and the secondary outcome was the rate of adverse effects of cardiac origin due to vernakalant and the placebo or amiodarone. Meta-analyses were carried out using Mantel-Haenszel fixed-effects or random-effects models and heterogeneity was by the X² test. Conclusion: In the conversion of AF to SR, vernakalant is highly effective without obviously raised side effects. Owing to only one study comparing vernakalant with amiodarone included in this study, the efficacy of vernakalant comparing to other antiarrhythmic agents needing more well-designed double-blinded RCTs to be confirmed. [ABSTRACT FROM AUTHOR]

Using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model to obtain backward trajectories and then conduct clustering analysis is a common method to analyze potential sources and transmission paths of atmospheric particulate pollutants. Taking Qingdao (N36 E120) as an example, the global data assimilation system (GDAS 1°) of days from 2015 to 2018 provided by National Centers for Environmental Prediction (NCEP) is used to process the backward 72 h trajectory data of 3 arrival heights (10 m, 100 m, 500 m) through the HYSPLIT model with a data interval of 6 h (UTC 0:00, 6:00, 12:00, and 18:00 per day). Three common clustering methods of trajectory data, i.e., K-means, Hierarchical clustering (Hier), and Self-organizing maps (SOM), are used to conduct clustering analysis of trajectory data, and the results are compared with those of the HYSPLIT model released by National Oceanic and Atmospheric Administration (NOAA). Principal Component Analysis (PCA) is used to analyze the original trajectory data. The internal evaluation indexes of Davies–Bouldin Index (DBI), Silhouette Coefficient (SC), Calinski Harabasz Index (CH), and I index are used to quantitatively evaluate the three clustering algorithms. The results show that there is little information in the height data, and thus only two-dimensional plane data are used for clustering. From the results of clustering indexes, the clustering results of SOM and K-means are better than the Hier and HYSPLIT model. In addition, it is found that DBI and I index can help to select the number of clusters, of which DBI is preferred for cluster analysis. [ABSTRACT FROM AUTHOR]

Alfvén eigenmodes (AEs) and energetic particle modes (EPMs) have been observed in EAST neutral beam injection plasma with the presence of heavy tungsten impurity in the core region. The AEs are identified as toroidal Alfvén eigenmodes (TAEs) with roughly constant frequency fTAE ∼ 100 kHz and a special set of AEs with frequency chirping up from frequency fAE ∼ 70 kHz to fAE ∼ 100 kHz. The frequency of EPMs is around fEPM ∼ 60 kHz, exhibiting with frequency chirping down feature. A transition from EPMs to AEs is found, and the location and mode numbers are measured by experimental diagnostics. Hybrid simulations with the global kinetic-magnetohydrodynamic code M3D-K have been carried out to investigate the observed AEs and EPMs, and linear simulations find the EPM with toroidal mode number n = 2 and poloidal mode number m = 3. Nonlinear simulations show that the EPM frequency chirps down nonlinearly, and then two modes emerge with frequencies around 72.7 kHz and 88.0 kHz: the mode with higher frequency is a TAE, and the other mode is an EPM. Afterward, the TAE disappears quickly with the frequency almost unchanged, and the EPM with lower frequency chirps down continuously. The mode frequencies and locations of the simulated EPM and TAE, as well as the chirping down feature of the EPM frequency, are consistent with the experimental measurements. The transition from EPM to TAE is found to be a self-consistent dynamic of nonlinear evolution with the expel of energetic ions due to EPM. [ABSTRACT FROM AUTHOR]

Background: High power shorter duration (HPSD) ablation may lead to safe and rapid lesion formation. However, the optimal radio frequency power to achieve the desired ablation index (AI) or lesion size index (LSI) is insubstantial. This analysis aimed to appraise the clinical safety and efficacy of HPSD guided by AI or LSI (HPSD-AI or LSI) in patients with atrial fibrillation (AF).Methods: The Medline, PubMed, Embase, Web of Science, and the Cochrane Library databases from inception to November 2020 were searched for studies comparing HPSD-AI or LSI and low power longer duration (LPLD) ablation.Results: Seven trials with 1013 patients were included in the analysis. The analyses verified that HPSD-AI or LSI revealed benefits of first-pass pulmonary vein isolation (PVI) (RR: 1.28; 95% CI: 1.05-1.56, P = 0.01) and acute pulmonary vein reconnection (PVR) (RR: 0.65; 95% CI: 0.48-0.88, P = 0.005) compared with LPLD. HPSD-AI or LSI showed higher freedom from atrial tachyarrhythmia (AT) (RR = 1.32, 95% CI: 1.14-1.53, P = 0.0002) in the subgroup analysis of studies with PVI ± (with or without additional ablation beyond PVI). HPSD-AI or LSI could short procedural time (WMD: -22.81; 95% CI, -35.03 to -10.60, P = 0.0003), ablation time (WMD: -10.80; 95% CI: -13.14 to -8.46, P < .00001), and fluoroscopy time (WMD: -7.71; 95% CI: -13.71 to -1.71, P = 0.01). Major complications and esophageal lesion in HPSD-AI or LSI group were no more than LDLP group (RR: 0.58; 95% CI: 0.20-1.69, P = 0.32) and (RR: 0.84; 95% CI: 0.43-1.61, P = 0.59).Conclusions: HPSD-AI or LSI was efficient for treating AF with shorting procedural, ablation, and fluoroscopy time, higher first-pass PVI, and reducing acute PVR and may increase freedom from AT for patients with additional ablation beyond PVI compared with LPLD. Moreover, complications and esophageal lesion were low and no different between two groups. [ABSTRACT FROM AUTHOR]

Fusion neutron activation experiments have been carried out to investigate the activation of metal materials in Experimental Advanced Superconducting Tokamak (EAST). Some cylindrical samples made of stainless steel 316L, carbon steel (A3), high purity cadmium (Cd), oxygen-free copper (Cu), and high purity aluminum (Al) were transported to Port G in the EAST hall for neutron irradiation. The characteristic gamma rays emitted by activated samples were measured with a spectrometer consisting of a digital multichannel analyzer (MCA) and a high purity germanium (HPGe) detector. Qualitative identification of the dominant radionuclides and quantitative measurement of the specific activity have been performed. The experimental results indicated that the high radioactive nuclides with short half-life were produced, such as 56Mn in 316L and A3, 28Al in Al, 66Cu in Cu, and 111m Cd in Cd. Moreover, the long half-life radionuclides were generated in 316L, A3, Cu and Cd samples, which will become the major factors on environment and personnel. In addition, the normalized dose rate at 10 cm in the air of 316L, A3, Al, Cd, and Cu was 1.53 × 10−23, 4.83 × 10−24, 5.57 × 10−23, 4.90 × 10−24 and 1.10 × 10−23 Sv/h/n, respectively. Finally, the comparison between the experimental neutron activation results and the predicted quantities calculated with FISPACT-2007 was discussed. [ABSTRACT FROM AUTHOR]