As one important component of sulfur cathodes, the carbon host plays a key role in the electrochemical performance of lithium-sulfur (Li-S) batteries. In this paper, a mesoporous nitrogen-doped carbon (MPNC)-sulfur nanocomposite is reported as a novel cathode for advanced Li-S batteries. The nitrogen doping in the MPNC material can effectively promote chemical adsorption between sulfur atoms and oxygen functional groups on the carbon, as verifi ed by X-ray absorption near edge structure spectroscopy, and the mechanism by which nitrogen enables the behavior is further revealed by density functional theory calculations. Based on the advantages of the porous structure and nitrogen doping, the MPNC-sulfur cathodes show excellent cycling stability (95% retention within 100 cycles) at a high current density of 0.7 mAh cm −2 with a high sulfur loading (4.2 mg S cm −2 ) and a sulfur content (70 wt%). A high areal capacity (≈3.3 mAh cm −2 ) is demonstrated by using the novel cathode, which is crucial for the practical application of Li-S batteries. It is believed that the important role of nitrogen doping promoted chemical adsorption can be extended for development of other high performance carbon-sulfur composite cathodes for Li-S batteries.

Nitrogen-Doped Mesoporous Carbon Promoted Chemical Adsorption of Sulfur and Fabrication of High-Areal-Capacity Sulfur Cathode with Exceptional Cycling Stability for Lithium-Sulfur Batteries

As emerging crystalline porous organic-inorganic hybrid materials, metal-organic frameworks (MOFs) have been widely used as sacrificial precursors for the synthesis of carbon materials, metal/metal compounds, and their composites with tunable and controllable nanostructures and chemical compositions for electrochemical energy applications. Herein, recent progress of MOF-derived nanomaterials for various electrochemical energy storage and conversion applications including Li-ion batteries, Li-S batteries, Na-ion batteries, supercapacitors, water splitting, and oxygen reduction reaction is reviewed. Structural and compositional design of MOF-derived nanomaterials is systematically summarized, which may hopefully offer inspirations and guidances for future development of MOF-derived nanomaterials for more efficient and more durable electrochemical energy applications.

Metal-organic framework-derived materials for electrochemical energy applications

Interlayer Material Selection for Lithium-Sulfur Batteries

Fourth universal definition of myocardial infarction. focus on the type 2 myocardial infarction

Activity recognition plays an essential role in bridging the gap between the low-level sensor data and the high-level applications in ambient-assisted living systems. With the aim to obtain satisfactory recognition rate and adapt to various application scenarios, a variety of sensors have been exploited, among which, smartphone-embedded inertial sensors are widely applied due to its convenience, low cost, and intrusiveness. In this paper, we explore the power of triaxial accelerometer and gyroscope built-in a smartphone in recognizing human physical activities in situations, where they are used simultaneously or separately. A novel feature selection approach is then proposed in order to select a subset of discriminant features, construct an online activity recognizer with better generalization ability, and reduce the smartphone power consumption. Experimental results on a publicly available data set show that the fusion of both accelerometer and gyroscope data contributes to obtain better recognition performance than that of using single source data, and that the proposed feature selector outperforms three other comparative approaches in terms of four performance measures. In addition, great improvement in time performance can be achieved with an effective feature selector, indicating the way of power saving and its applicability to real-world activity recognition.

A Comparative Study on Human Activity Recognition Using Inertial Sensors in a Smartphone

Falls are one of the major causes leading to injury of elderly people. Using wearable devices for fall detection has a high cost and may cause inconvenience to the daily lives of the elderly. In this paper, we present an automated fall detection approach that requires only a low-cost depth camera. Our approach combines two computer vision techniques-shape-based fall characterization and a learning-based classifier to distinguish falls from other daily actions. Given a fall video clip, we extract curvature scale space (CSS) features of human silhouettes at each frame and represent the action by a bag of CSS words (BoCSS). Then, we utilize the extreme learning machine (ELM) classifier to identify the BoCSS representation of a fall from those of other actions. In order to eliminate the sensitivity of ELM to its hyperparameters, we present a variable-length particle swarm optimization algorithm to optimize the number of hidden neurons, corresponding input weights, and biases of ELM. Using a low-cost Kinect depth camera, we build an action dataset that consists of six types of actions (falling, bending, sitting, squatting, walking, and lying) from ten subjects. Experimenting with the dataset shows that our approach can achieve up to 91.15% sensitivity, 77.14% specificity, and 86.83% accuracy. On a public dataset, our approach performs comparably to state-of-the-art fall detection methods that need multiple cameras.

Depth-based human fall detection via shape features and improved extreme learning machine

Ultrafine TiO2 confined in porous-nitrogen-doped carbon is synthesized from a single metal–organic framework precursor. As a novel interlayer for lithium–sulfur batteries, the TiO2@NC composite can act as both a high efficiency lithium polysulfide barrier to suppress the side reactions and an additional current collector to enhance the polysulfide redox reactions. The lithium–sulfur battery with a TiO2@NC interlayer delivers a high reversible capacity of 1460 mAh g–1 at 0.2 C and capacity retention of 71% even after 500 cycles with high rate capability.

Ultrafine TiO2 Confined in Porous-Nitrogen-Doped Carbon from Metal–Organic Frameworks for High-Performance Lithium Sulfur Batteries

Unsteady aerodynamic model of flexible flapping wing

Multiple myeloma (MM) is the second most common haematological malignancy and results in destructive bone lesions. The interaction between MM cells and the bone microenvironment plays an important role in the development of the tumour cells and MM-induced bone disease and forms a 'vicious cycle' of tumour development and bone destruction, intensified by suppression of osteoblast activity and promotion of osteoclast activity. In this paper, a mathematical model is proposed to simulate how the interaction between MM cells and the bone microenvironment facilitates the development of the tumour cells and the resultant bone destruction. It includes both the roles of inhibited osteoblast activity and stimulated osteoclast activity. The model is able to mimic the temporal variation of bone cell concentrations and resultant bone volume after the invasion and then removal of the tumour cells and explains why MM-induced bone lesions rarely heal even after the complete removal of MM cells. The behaviour of the model compares well with published experimental data. The model serves as a first step to understand the development of MM-induced bone disease and could be applied further to evaluate the current therapies against MM-induced bone disease and even suggests new potential therapeutic targets.

Mathematical modelling of the pathogenesis of multiple myeloma‐induced bone disease

As a severe hazard in coal mining and rock excavation, the rockburst is usually induced by the high energy tremor. Microseismic (MS) monitoring is suggested to forecast the rockburst risk to reduce its damage. The paper aims to investigate contribution of multi-MS data, including MS raw wave data and MS energy data, to prediction of the high energy tremor, using support vector machine (SVM) together with genetic algorithm (GA). MS monitoring data recorded for more than 400 days at Wudong coal mine of Xinjiang, China, were used in the paper. 132 and 24 features are initially extracted from MS raw wave and energy data in the frequency domain, entropy and time-frequency domain, respectively. GA is not only used to select effective ones among initially extracted features, but also optimize hyperparameters for SVM to classify high energy tremors from general MS events. The performances of the proposed approach based on multi-MS data are evaluated by cross-validation. The results show that the classifier achieves 98% sensitivity, 88% accuracy and 87% specificity using both MS raw wave and energy data, which is better than solely utilizing MS raw wave (98% sensitivity, 84% accuracy and 83% specificity) or energy data (98% sensitivity, 86% accuracy and 85% specificity). These findings suggest that MS raw wave data makes important contribution to rockburst risk prediction as well as MS energy data, and the better performance can be achieved when utilizing two kinds of data simultaneously.

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Bing Ji

Papers

Depth-based human fall detection via shape features and improved extreme learning machine

Ultrafine TiO2 Confined in Porous-Nitrogen-Doped Carbon from Metal–Organic Frameworks for High-Performance Lithium Sulfur Batteries

Unsteady aerodynamic model of flexible flapping wing

Mathematical modelling of the pathogenesis of multiple myeloma‐induced bone disease

Investigate Contribution of Multi-Microseismic Data to Rockburst Risk Prediction Using Support Vector Machine With Genetic Algorithm