The synthetic methodology for direct indole functionalizations is of great significance in indole chemistry and has been intensively investigated in the last few decades. From the perspective of ...green chemistry, oxygen is the best choice as the terminal oxidant in molecular synthesis. Hence, aerobic oxidative functionalization of indoles became a hot research topic in the last decade. Numerous efficient protocols in this field have been discovered that enable facile and efficient transformations of indoles to related valuable compounds, which are summarized and discussed in detail in this review.
High-entropy pyrochlore-type structures based on rare-earth zirconates are successfully produced by conventional solid-state reaction method. Six rare-earth oxides (La
2
O
3
, Nd
2
O
3
, Sm
2
O
3
, ...Eu
2
O
3
, Gd
2
O
3
, and Y
2
O
3
) and ZrO
2
are used as the raw powders. Five out of the six rare-earth oxides with equimolar ratio and ZrO
2
are mixed and sintered at different temperatures for investigating the reaction process. The results demonstrate that the high-entropy pyrochlores (5RE
1/5
)
2
Zr
2
O
7
have been formed after heated at 1000°C. The (5RE
1/5
)
2
Zr
2
O
7
are highly sintering resistant and possess excellent thermal stability. The thermal conductivities of the (5RE
1/5
)
2
Zr
2
O
7
high-entropy ceramics are below 1 W·m
–1
·K
–1
in the temperature range of 300–1200°C. The (5RE
1/5
)
2
Zr
2
O
7
can be potential thermal barrier coating materials.
Human motion prediction aims to generate future motions based on the observed human motions. Witnessing the success of Recurrent Neural Networks (RNN) in modeling sequential data, recent works ...utilize RNNs to model human-skeleton motions on the observed motion sequence and predict future human motions. However, these methods disregard the existence of the spatial coherence among joints and the temporal evolution among skeletons, which reflects the crucial characteristics of human motions in spatiotemporal space. To this end, we propose a novel Skeleton-Joint Co-Attention Recurrent Neural Networks (SC-RNN) to capture the spatial coherence among joints, and the temporal evolution among skeletons simultaneously on a skeleton-joint co-attention feature map in spatiotemporal space. First, a skeleton-joint feature map is constructed as the representation of the observed motion sequence. Second, we design a new Skeleton-Joint Co-Attention (SCA) mechanism to dynamically learn a skeleton-joint co-attention feature map of this skeleton-joint feature map, which can refine the useful observed motion information to predict one future motion. Third, a variant of GRU embedded with SCA collaboratively models the human-skeleton motion and human-joint motion in spatiotemporal space by regarding the skeleton-joint co-attention feature map as the motion context. Experimental results of human motion prediction demonstrate that the proposed method outperforms the competing methods.
In this paper, we present a label transfer model from texts to images for image classification tasks. The problem of image classification is often much more challenging than text classification. On ...one hand, labeled text data is more widely available than the labeled images for classification tasks. On the other hand, text data tends to have natural semantic interpretability, and they are often more directly related to class labels. On the contrary, the image features are not directly related to concepts inherent in class labels. One of our goals in this paper is to develop a model for revealing the functional relationships between text and image features as to directly transfer intermodal and intramodal labels to annotate the images. This is implemented by learning a transfer function as a bridge to propagate the labels between two multimodal spaces. However, the intermodal label transfers could be undermined by blindly transferring the labels of noisy texts to annotate images. To mitigate this problem, we present an intramodal label transfer process, which complements the intermodal label transfer by transferring the image labels instead when relevant text is absent from the source corpus. In addition, we generalize the inter-modal label transfer to zero-shot learning scenario where there are only text examples available to label unseen classes of images without any positive image examples. We evaluate our algorithm on an image classification task and show the effectiveness with respect to the other compared algorithms.
An iodine‐catalyzed sulfenylation of free indoles with sodium sulfinates is described. The reaction selectively afforded 3‐arylthioindoles in good to high yields in anisole under metal‐free ...conditions. Functional groups such as halogens were well tolerated under the optimized reaction conditions.
A high-entropy silicide (HES), (Ti
0.2
Zr
0.2
Nb
0.2
Mo
0.2
W
0.2
)Si
2
with close-packed hexagonal structure is successfully manufactured through reactive spark plasma sintering at 1300 °C for 15 ...min. The elements in this HES are uniformly distributed in the specimen based on the energy dispersive spectrometer analysis except a small amount of zirconium that is combined with oxygen as impurity particles. The Young’s modulus, Poisson’s ratio, and Vickers hardness of the obtained (Ti
0.2
Zr
0.2
Nb
0.2
Mo
0.2
W
0.2
)Si
2
are also measured.
It is thought that the sintering of high‐entropy (HE) ceramics is generally more difficult when compared to that of the corresponding single‐component ceramics. In this paper, we report a novel ...approach to densify the HE carbide ceramics at relatively low temperatures with a small amount of silicon. Reactive spark plasma sintering (SPS) was used to densify the ceramics using powders of HE carbide and silicon as starting materials. Dense ceramics can be obtained at 1600 ‐1700°C. X‐ray diffraction analysis reveals that only non‐stoichiometric HE carbide phase with carbon vacancy and SiC phase exist in the obtained ceramics. The in‐situ formed SiC phase inherits the morphology of the starting silicon powder owing to the slower diffusion of silicon atoms compared to that of the carbon atoms in HE carbide phase. The mechanical properties of the prepared ceramics were preliminarily studied.
Ultra-high temperature ceramics (UHTCs) are generally referred to the carbides, nitrides, and borides of the transition metals, with the Group IVB compounds (Zr & Hf) and TaC as the main focus. The ...UHTCs are endowed with ultra-high melting points, excellent mechanical properties, and ablation resistance at elevated temperatures. These unique combinations of properties make them promising materials for extremely environmental structural applications in rocket and hypersonic vehicles, particularly nozzles, leading edges, and engine components, etc. In addition to bulk UHTCs, UHTC coatings and fiber reinforced UHTC composites are extensively developed and applied to avoid the intrinsic brittleness and poor thermal shock resistance of bulk ceramics. Recently, highentropy UHTCs are developed rapidly and attract a lot of attention as an emerging direction for ultra-high temperature materials. This review presents the state of the art of processing approaches, microstructure design and properties of UHTCs from bulk materials to composites and coatings, as well as the future directions.
In this paper we present new methods for solving multi-criteria decision-making problem in an intuitionistic fuzzy environment. First, we define an evaluation function for the decision-making problem ...to measure the degrees to which alternatives satisfy and do not satisfy the decision-maker’s requirement. Then, we introduce and discuss the concept of intuitionistic fuzzy point operators. By using the intuitionistic fuzzy point operators, we can reduce the degree of uncertainty of the elements in a universe corresponding to an intuitionistic fuzzy set. Furthermore, a series of new score functions are defined for multi-criteria decision-making problem based on the intuitionistic fuzzy point operators and the evaluation function and their effectiveness and advantage are illustrated by examples.
In this work, we aim to address the problem of human interaction recognition in videos by exploring the long-term inter-related dynamics among multiple persons. Recently, Long Short-Term Memory ...(LSTM) has become a popular choice to model individual dynamic for single-person action recognition due to its ability to capture the temporal motion information in a range. However, most existing LSTM-based methods focus only on capturing the dynamics of human interaction by simply combining all dynamics of individuals or modeling them as a whole. Such methods neglect the inter-related dynamics of how human interactions change over time. To this end, we propose a novel Hierarchical Long Short-Term Concurrent Memory (H-LSTCM) to model the long-term inter-related dynamics among a group of persons for recognizing human interactions. Specifically, we first feed each person's static features into a Single-Person LSTM to model the single-person dynamic. Subsequently, at one time step, the outputs of all Single-Person LSTM units are fed into a novel Concurrent LSTM (Co-LSTM) unit, which mainly consists of multiple sub-memory units, a new cell gate, and a new co-memory cell. In the Co-LSTM unit, each sub-memory unit stores individual motion information, while this Co-LSTM unit selectively integrates and stores inter-related motion information between multiple interacting persons from multiple sub-memory units via the cell gate and co-memory cell, respectively. Extensive experiments on several public datasets validate the effectiveness of the proposed H-LSTCM by comparing against baseline and state-of-the-art methods.
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