Optimization of desert lake information extraction from remote sensing images using cellular automata

Desert lakes are important wetland resources in the blown-sand area of western China and play a significant role in maintain-ing the regional ecological environment.However,large-scale coal mining in recent years has considerably impacted the deposition condition of several lakes.Rapid and accurate extraction of lake information based on satellite images is crucial for developing protective measures against desertification.However,the spatial resolution of these images often leads to mixed pixels near water boundaries,affecting extraction precision.Traditional pixel unmixing methods mainly obtain water coverage information in a mixed pixel,making it difficult to accurately describe the spatial distribution.In this paper,the cellular automata(CA)model was adopted in order to realize lake information extraction at a sub-pixel level.A mining area in Shenmu City,Shaanxi Province,China is selected as the research region,using the image of Sentinel-2 as the data source and the high spatial resolution UAV image as the reference.First,water coverage of mixed pixels in the Sentinel-2 image was calculated with the dimidiate pixel model and the fully constrained least squares(FCLS)method.Second,the mixed pixels were subdivided to form the cellular space at a sub-pixel level and the transition rules are constructed based on the water coverage information and spatial correlation.Lastly,the process was implemented using Python and IDL,with the ArcGIS and ENVI software being used for validation.The experiments show that the CA model can improve the sub-pixel positioning accuracy for lake bodies in mixed pixel image and improve classification accuracy.The FCLS-CA model has a higher accuracy and is able to identify most water bodies in the study area,and is therefore suitable for desert lake monitor-ing in mining areas.

An Improved RPL Algorithm for Low-Power and Lossy Networks

The routing protocol for low-power and lossy networks(RPL),standardized by Internet Engineering Task Force(IETF),is mainly designed to use for Low-power and Lossy Networks(LLNs).To solve the problems of several important routing metrics are not evaluated,the optimal path may contain long single hop links,lack of scientific multi-routing metrics evaluation method and mechanism to balance the parent child number(especially the parent with one hop away from root),this paper proposes an improved RPL algorithm for LLN(I-RPL).First of all,we propose the evaluated routing metrics:child number of parent,candidate parent number,hop count,ETX and energy consumption index.Meanwhile,we improve the path ETX calculation method to avoid selecting optimal path containing long single hop links.Then we design a novel lexical method to synthetically evaluate candidate parents.Meanwhile,based on the evaluation results of candidate parents,we design a novel objective function and a new calculation node rank method which can also be used for selecting the optimal path.Finally,evaluation results show that I-RPL outperforms ETXOF and several other improvements in terms of packet delivery ratio,latency,etc.

肿瘤微环境中脂肪细胞对肺腺癌A549细胞生物学行为影响的初步研究

背景与目的脂肪细胞在肿瘤微环境中可通过提供代谢燃料或信号传导媒介等方式以促进多种恶性肿瘤细胞的增殖与侵袭,但其在肺癌进展过程中的作用尚不清楚。本研究旨在探讨脂肪细胞对肺癌细胞生物学行为的影响。方法将3T3-L1前脂肪细胞诱导为成熟脂肪细胞;采用显微镜成像、油红O染色试验观察细胞形态;应用MTT、平板克隆形成实验、划痕实验以及Transwell检测肺癌细胞增殖、迁移及侵袭能力的变化;通过比色分析法测定细胞内甘油三酯的含量。结果与成熟脂肪细胞共培养后,肺腺癌A549细胞的形态变得更加纤长,增殖与克隆形成能力明显增强(P<0.05);此外,成熟脂肪细胞亦可促进A549细胞的迁移(P<0.05)、侵袭(P<0.01)以及胞内脂质的累积(P<0.05)。结论肿瘤微环境中的脂肪细胞可促进肺腺癌A549细胞的增殖、迁移与侵袭,且这一促进作用可能与脂质代谢相关。

RPL Based on Triangle Module Operator for AMI Networks

the routing protocol for low-power and lossy networks(RPL) has been used in advanced metering infrastructure(AMI)which could provide two-way communication between smart meters and city utilities.To improve the network performance of AMI networks, this paper proposed an improved algorithm of RPL based on triangle module operator(IAR-TMO). IAR-TMO proposes membership functions of the following five typical routing metrics: end-to-end delay, number of hops, expected transmission count(ETX),node remaining energy, and child node count.Moreover, IAR-TMO uses triangle module operator to fuse membership functions of these routing metrics. Then, IAR-TMO selects preferred parents(the next hop) based on the triangle module operator. Theoretical analysis and simulation results show that IAR-TMO has a great improvement when compared with two recent representative algorithms: ETXOF(ETX Objective Function) and OF-FL(Objective Function based on Fuzzy Logic), in terms of network lifetime, average end-to-end delay,etc. Consequently, the network performances of AMI networks can be improved effectively.

学习型智能交互系统的研究与实现

本文是以研究开发一款可实现学习型智能交互的软件为目的,在人机交互的基础上利用JAVA等编程语言实现文字、语音、表情三方面的交互,同时更具有自学习的功能,并将自学习反过来利用到交互系统,具有很好的软件灵活性。本文主要介绍一种具有学习能力的智能交互的设计与实现技术。

Multi-omics analyses of G6PD deficiency variants in Chinese population

Glucose-6-phosphate dehydrogenase(G6PD)deficiency is one of the most prevalent hereditary and X-linked enzyme disorders caused by the pathogenetic G6PD(NM_001042351.2)variants(Yang et al.,2016).The clinical manifestations of G6PD deficiency offer a wide spectrum of diverse disease phenotypes(Hecker et al.,2013).Investigations of prevalence and molecular epidemiology demonstrate that G6PD deficiency affects over 400 milion individuals worldwide and leads to thousands of deaths annually(Mortality and Causes of Death,2015).

Effects of residual organics in municipal wastewater on hydrogenotrophic denitrifying microbial communities

Hydrogenotrophic denitrification is promising for tertiary nitrogen removal from municipal wastewater. To reveal the influence of residual organics in municipal wastewater on hydrogenotrophic denitrifiers, we adopted high-throughput 16 S r RNA gene amplicon sequencing to examine microbial communities in hydrogenotrophic denitrification enrichments. Using effluent from a municipal wastewater treatment plant as water source, COD,nitrate and p H were controlled the same except for a gradient of biodegradable carbon（i.e., primary effluent（PE）, secondary effluent（SE）, or combined primary and secondary effluent（CE））. Inorganic synthetic water（IW） was used as a control. Hydrogenophaga, a major facultative autotroph, accounted for 17.1%, 5.3%, 32.7% and 12.9% of the sequences in PE, CE,SE and IW, respectively, implicating that Hydrogenophaga grew well with or without organics.Thauera, which contains likely obligate autotrophic denitrifiers, appeared to be the most dominant genera（23.6%） in IW and accounted for 2.5%, 4.6% and 8.9% in PE, CE and SE,respectively. Thermomonas, which is related to heterotrophic denitrification, accounted for 4.2% and 7.9% in PE and CE fed with a higher content of labile organics, respectively.In contrast, Thermomonas was not detected in IW and accounted for only 0.6% in SE. Our results suggest that Thermomonas are more competitive than Thauera in hydrogenotrophic denitrification with biodegradable organics. Moreover, facultative autotrophic denitrifiers,Hydrogenophaga, are accommodating to residual organic in effluent wastewater, thus we propose that hydrogenotrophic denitrification is amenable for tertiary nitrogen removal.

Boosting imbalanced data learning with Wiener process oversampling

Learning from imbalanced data is a challenging task in a wide range of applications, which attracts significant research efforts from machine learning and data mining community. As a natural approach to this issue, oversampling balances the training samples through replicating existing samples or synthesizing new samples. In general, synthesization outperforms replication by supplying additional information on the minority class. However, the additional information needs to follow the same normal distribution of the training set, which further constrains the new samples within the predefined range of training set. In this paper, we present the Wiener process oversampling （WPO） technique that brings the physics phenomena into sample synthesization. WPO constructs a robust decision region by expanding the attribute ranges in training set while keeping the same normal distribution. The satisfactory performance of WPO can be achieved with much lower computing complexity. In addition, by integrating WPO with ensemble learning, the WPOBoost algorithm outperforms many prevalent imbalance learning solutions.

A novel context-aware RPL algorithm based on a triangle module operator

For the use in low-power and lossy networks(LLNs)under complex and harsh communication conditions,the routing protocol for LLNs(RPL)standardized by the Internet Engineering Task Force is specially designed.To improve the performance of LLNs,we propose a novel context-aware RPL algorithm based on a triangle module operator(CAR-TMO).A novel composite context-aware routing metric(CA-RM)is designed,which synchronously evaluates the residual energy index,buffer occupancy ratio of a node,expected transmission count(ETX),delay,and hop count from a candidate parent to the root.CA-RM considers the residual energy index and buffer occupancy ratio of the candidate parent and its preferred parent in a recursive manner to reduce the effect of upstream parents,since farther paths are considered.CA-RM comprehensively uses the sum,mean,and standard deviation values of ETX and delay of links in a path to ensure a better performance.Moreover,in CAR-TMO,the membership function of each routing metric is designed.Then,a comprehensive membership function is constructed based on a triangle module operator,the membership function of each routing metric,and a comprehensive context-aware objective function.A novel mechanism for calculating the node rank and the mechanisms for preferred parent selection are proposed.Finally,theoretical analysis and simulation results show that CAR-TMO outperforms several state-of-the-art RPL algorithms in terms of the packet delivery ratio and energy efficiency.