Maximum Data Generation Rate Routing Protocol Based on Data Flow Controlling Technology for Rechargeable Wireless Sensor Networks

For rechargeable wireless sensor networks, limited energy storage capacity, dynamic energy supply, low and dynamic duty cycles cause that it is unpractical to maintain a fixed routing path for packets delivery permanently from a source to destination in a distributed scenario. Therefore, before data delivery, a sensor has to update its waking schedule continuously and share them to its neighbors, which lead to high energy expenditure for reestablishing path links frequently and low efficiency of energy utilization for collecting packets. In this work, we propose the maximum data generation rate routing protocol based on data flow controlling technology. For a sensor, it does not share its waking schedule to its neighbors and cache any waking schedules of other sensors. Hence, the energy consumption for time synchronization, location information and waking schedule shared will be reduced significantly. The saving energy can be used for improving data collection rate. Simulation shows our scheme is efficient to improve packets generation rate in rechargeable wireless sensor networks.     

TiO2/MXene-Assisted LDI-MS for Urine Metabolic Profiling in Urinary Disease

Cancer remains an intractable medical problem. Rapid diagnosis and identification of cancer are critical to differentiate it from nonmalignant diseases. High-throughput biofluid metabolic analysis has potential for cancer diagnosis. Nevertheless, the present metabolite analysis method does not meet the demand for high-throughput screening of diseases. Herein, a high-throughput, cost-effective, and noninvasive urine metabolic profiling method based on TiO₂/MXene-assisted laser desorption/ionization mass spectrometry (LDI-MS) is presented for the efficient screening of bladder cancer (BC) and nonmalignant urinary disease. Combined with machine learning, TiO₂/MXene-assisted LDI-MS enables high diagnostic accuracy (96.8%) for the classification of patient groups (including 47 BC and 46 ureteral calculus (UC) patients) from healthy controls (113 cases). In addition, BC patients can also be identified from noncancerous UC individuals with an accuracy of 88.3% in the independent test cohort. Furthermore, metabolite variations between BC and UC individuals are investigated based on relative quantification, and related pathways are also discussed. These results suggest that this method, based on urine metabolic patterns, provides a potential tool for rapidly distinguishing urinary diseases and it may pave the way for precision medicine.     

基于ABAQUS的显式动力学分析方法研究

显式动力学(The Explicit Dynamic)是针对隐式求解器的一个补充,其分析方法对于求解广泛、各类非线性结构力学问题是一个非常有效的工具,显式方法中的单个增量步取决于模型的最高固有频率,与持续时间、载荷类型无关。ABAQUS/Explicit主要用于碰撞、接触以及失效分析。探讨了ABAQUS显式动力学的基本理论、求解的问题,对显示动力学分析方法中的求解算法、稳定时间极限和能量平衡问题进行研究,给出了显示动力学分析的一般性分析方法。通过案例验证了该方法的可行性,应用该方法可有效解决实际工业生产中的碰撞问题、复杂的接触问题以及复杂的后屈曲问题,可为显式动力学分析方法应用于工程实践奠定理论与方法基础。     

A modified cross-correlation algorithm for PIV image processing of particle-fluid two-phase flow

PIV (Particle Image Velocimetry) technique for flow field measurement has achieved popular self-identify through over ten years development, and its application range is becoming wider and wider. PIV post-processing techniques have a great influence on the success of particle-fluid two-phase flow field measurement and thus become a hot and difficult topic. In the present study, a Phase Respective Identification Algorithm (PRIA) is introduced to separate low-density solid particles or bubbles and high-density tracer particles from the PIV image of particle-fluid two-phase flow. PTV (Particle Tracking Velocimetry) technique is employed to calculate the velocity fields of low-density solid particles or bubbles. For the velocity fields of high-density solid particles or bubble phase and continuous phase traced by high-density smaller particles, based on the thought of wavelet transform and multi-resolution analysis and the theory of cross-correlation of image, a delaminated processing algorithm (MCCWM) is presented to conquer the limitation of conventional Fourier transform. The algorithm is firstly testified on synthetic two-phase flows, such as uniform steady flow, shearing flow and rotating flow, and the computational results from the simulated particle images are in reasonable agreement with the given simulated data. The algorithm is then applied to images of actual bubble-liquid two-phase flow and jet flow, and the results also confirmed that the algorithm proposed in the present study has good performance and reliability for post-processing PIV images of particle-fluid two-phase flow.     

Quantitative UPLC‐MS/MS analysis of chlorogenic acid derivatives in antioxidant fractionates from dandelion (Taraxacum officinale) root

While qualitative studies have identified chlorogenic acids in antioxidant extracts, particularly ethyl acetate‐derived extracts, of Taraxacum officinale, quantitative analysis of these phenolic compounds remains largely unreported for this species. In this study, bioactivity‐guided fractionation of an antioxidant crude ethyl acetate extract (DPPH = 295.481 ± 0.955 mg TE g^?1 extract) from T. officinale root resulted in a number of reverse‐phase fractions that demonstrated high antioxidant activity (DPPH = 1058.733–1312.136 mg TE g^?1 extract), stronger than that of the synthetic antioxidant Trolox^®. UPLC‐MS/MS screening of these fractions for the presence of selected mono‐ and di‐caffeoylquinic acids revealed large quantities of 1,5‐dicaffeoylquinic acid present in several fractions (853.052–907.324 μg mg^?1), respectively. Due to the antioxidant potency and high levels of 1,5‐dicaffeoylquinic acid observed in these fractions, it was concluded that specifically this chlorogenic acid derivative is a major contributor to the antioxidant efficacy of dandelion root.     

New anti-ablation candidate for carbon/carbon composites: Preparation,composition and ablation behavior of Y2Hf2O7 coating under an oxyacetylene torch

Y₂Hf₂O₇ possesses low thermal conductivity and high melting point, which make it promising for a new anti-ablation material. For evaluating the thermal stability and the potential applications of Y₂Hf₂O₇ on anti-ablation protection of C/C composites, Y₂Hf₂O₇ ceramic powder was synthesized by solution combustion method and Y₂Hf₂O₇ coating was prepared on the surface of SiC coated C/C composites using SAPS. Results shown that the coating exhibits good ablation resistance under the heat flux of 2.4?MW/m² with the linear and mass ablation rates are 0.16?μm?s^?1 and ?0.028?mg?s^?1, respectively, after ablation for 40?s. With the prolonging of the ablation time, the increasing thermal stress causes the increase of cracks. Moreover, the chemical erosion from SiO₂ and the physical volatilization of low temperature molten products aggravate failure of the Y₂Hf₂O₇ coating.     

Super-oleophilicity Co-doping Co2+/F-/TiO2 one-dimensional nanotubes for photocatalytic denitrification of light oil

In this study, a simple hydrothermal synthesis method was adapted for the preparation of Co-doping Co²⁺/F^-/TiO₂ nanotubes photocatalyst, and the micro-nano structure of catalysts prepared by biomimetic technology which makes the catalyst have super-oleophilicity property. Co²⁺/F^-/TiO₂ revealed improved photocatalytic performance for denitrification of light oil compared to single TiO₂ photocatalysts. The enhance of photocatalytic activity can be attributed to narrowing the band gap, increasing the light response wavelength and exposing more highly active crystal surfaces due to synergistic effects of Co²⁺ and F^? in the photocatalyst.     

基于MF-R和AWS密钥管理机制的物联网健康监测大数据分析系统

带有传感器的可穿戴式医疗设备不断生成大量数据，由于数据的复杂性，难以通过处理和分析大数据来找到有价值的决策信息。为了解决这个问题，提出了一种新的物联网体系结构，用于存储和处理医疗应用的可扩展传感器数据（大数据）。所提出的架构主要由两个子架构组成：Meta Fog重定向（MF-R）架构和AWS密钥管理机制。MF-R架构使用Apache Pig和Apache HBase等大数据技术来收集和存储不同传感器设备生成的传感器数据，并利用卡尔曼滤波消除噪声。AWS密钥管理机制使用密钥管理方案，目的是保护云中的数据，防止未经授权的访问。当数据存储在云中时，所提出的系统能够使用随机梯度下降算法和逻辑回归来开发心脏病的预测模型。仿真实验表明，和其他几种算法相比，提出的算法具有更小的误差，且在吞吐量、准确度等方面具有一定的优越性。     

Artificial bee colony based algorithm for maximum power point tracking (MPPT) for PV systems operating under partial shaded conditions

Artificial bee colony (ABC) algorithm has several characteristics that make it more attractive than other bio-inspired methods. Particularly, it is simple, it uses fewer control parameters and its convergence is independent of the initial conditions. In this paper, a novel artificial bee colony based maximum power point tracking algorithm (MPPT) is proposed. The developed algorithm, does not allow only overcoming the common drawback of the conventional MPPT methods, but it gives a simple and a robust MPPT scheme. A co-simulation methodology, combining Matlab/Simulink™ and Cadence/Pspice™, is used to verify the effectiveness of the proposed method and compare its performance, under dynamic weather conditions, with that of the Particle Swarm Optimization (PSO) based MPPT algorithm. Moreover, a laboratory setup has been realized and used to experimentally validate the proposed ABC-based MPPT algorithm. Simulation and experimental results have shown the satisfactory performance of the proposed approach.