Synthesis and electrochemical performance of hierarchical Sb2S3 nanorod-bundles for lithium-ion batteries

Uniform hierarchical Sb2S3 nanorod-bundles were synthesised successfully by L-cysteine hydrochloride-assisted solvothermal treatment, and were then characterised by X-ray diffraction, field emission scanning electron microscopy, and high-resolution transmission electron microscopy, respectively. The electrochemical performance of the synthesised Sb2S3 nanorod-bundles was investigated by cyclic voltammetry and galvanostatic charge−discharge technique, respectively. This material was found to exhibit a high initial charge specific capacity of 803 mA h g-1 at a rate of 100 mA g-1, a good cyclability of 614 mA h g-1 at a rate of 100 mA g-1 after 30 cycles, and a good rate capability of 400 mA h g-1 at a rate of 500 mA g-1 when evaluated as an electrode candidate material for lithium-ion batteries

A supramolecular assembly of {Fe10} molecular wheels with tubular structures

A 3-D supramolecular assembly of {Fe10} molecular wheels, [Fe(CH3O)2(O2CCH2O–Ph)]10·6H2O (1) with tubular structure was successfully prepared by a novel approach of simple modification of the building blocks for making symmetric clusters, Fe10. The diameter of the tubes is approximately 8.9 Å. Meanwhile, this tubular structure as the host traps hexameric clusters of water and the magnetic property of complex 1 shows that antiferromagnetic interactions exist between the high–spin iron(III) ions (S= 5/2) with J= 5.36 cm–1 and g= 2.03

Cationic liposomes as carriers for aerosolized formulations of an anionic drug: Safety and efficacy study

Photograph of Glenn A. Cox, President of Phillips Petroleum Company, at a Phillips Service Station

Mixed graph convolution and residual transformation network for skeleton-based action recognition

Action recognition based on a human skeleton is an extremely challenging research problem. The temporal information contained in the human skeleton is more difficult to extract than the spatial information. Many researchers focus on graph convolution networks and apply them to action recognition. In this study, an action recognition method based on a two-stream network called RNXt-GCN is proposed on the basis of the Spatial-Temporal Graph Convolutional Network (ST-GCN). The human skeleton is converted first into a spatial-temporal graph and a SkeleMotion image which are input into ST-GCN and ResNeXt, respectively, for performing the spatial-temporal convolution. The convolved features are then fused. The proposed method models the temporal information in action from the amplitude and direction of the action and addresses the shortcomings of isolated temporal information in the ST-GCN. The experiments are comprehensively performed on the four datasets: 1) UTD-MHAD, 2) Northwestern-UCLA, 3) NTU RGB-D 60, and 4) NTU RGB-D 120. The proposed model shows very competitive results compared with other models in our experiments. On the experiments of NTU RGB + D 120 dataset, our proposed model outperforms those of the state-of-the-art two-stream models

Impact of seasonal changes in urban green spaces with diverse vegetation structures on college students' physical and mental health

Abstract Based on the perceptions of college student participants in winter and summer, the effects of different vegetation structures within landscapes (single-layer woodland, tree-shrub-grass composite woodlands, tree-grass composite woodland, and single-layer grassland) and concrete squares without plants were investigated, and the skin conductivity level (SCL) and environmental perception recovery score (PRS) associated with landscape types were calculated. The results indicated that seasonal differences in landscape perception significantly affected college student participants' PRS but not their SCL scores, both in winter and summer. Viewing single-layer and tree-shrub-grass composite woodlands in summer, as well as single-layer woodland in winter, enhanced the environmental perception of the college student participants. The restorative effects of the four vegetation types in green spaces were ranked as follows: single-layer woodland, tree-shrub-grass composite woodlands, single-layer grassland, and tree-grass composite woodlands and concrete squares without plants. These findings underscore the importance of considering seasonal variations when choosing plant species for landscaping purposes, with evergreen single-layer woodland being a suitable choice for winter urban landscapes. This provides a scientific basis for assessing landscape perception and preferences in the future

N-Gram, Semantic-Based Neural Network for Mobile Malware Network Traffic Detection

Mobile malware poses a great challenge to mobile devices and mobile communication. With the explosive growth of mobile networks, it is significant to detect mobile malware for mobile security. Since most mobile malware relies on the networks to coordinate operations, steal information, or launch attacks, evading network monitor is difficult for the mobile malware. In this paper, we present an N-gram, semantic-based neural modeling method to detect the network traffic generated by the mobile malware. In the proposed scheme, we segment the network traffic into flows and extract the application layer payload from each packet. Then, the generated flow payload data are converted into the text form as the input of the proposed model. Each flow text consists of several domains with 20 words. The proposed scheme models the domain representation using convolutional neural network with multiwidth kernels from each domain. Afterward, relationships of domains are adaptively encoded in flow representation using gated recurrent network and then the classification result is obtained from an attention layer. A series of experiments have been conducted to verify the effectiveness of our proposed scheme. In addition, to compare with the state-of-the-art methods, several comparative experiments also are conducted. The experiment results depict that our proposed scheme is better in terms of accuracy