FUZZY APPROACHING SET AND FUZZYAPPROACHING FUNCTIONAL MAPPING

By establishing the concepts of fuzzy approaching set and fuzzy approaching functional mapping and making research on them, a new method for time series prediction is introduced.

Pattern recognition of seismogenic nodes using Kohonen selforganizing map: example in west and south west of Alborz region in Iran

Pattern recognition of seismic and mor- phostructural nodes plays an important role in seismic hazard assessment. This is a known fact in seismology that tectonic nodes are prone areas to large earthquake and have this potential. They are identified by morphostructural analysis. In this study, the Alborz region has considered as studied case and locations of future events are forecast based on Kohonen Self-Organized Neural Network. It has been shown how it can predict the location of earthquake, and identifies seismogenic nodes which are prone to earthquake of M5.5＋ at the West of Alborz in Iran by using International Institute Earthquake Engineering and Seismology earthquake catalogs data. First, the main faults and tectonic lineaments have been identified based on MZ （land zoning method） method. After that, by using pattern recognition, we generalized past recorded events to future in order to show the region of probable future earthquakes. In other word, hazardous nodes have determined among all nodes by new catalog generated Self-organizing feature maps （SOFM）. Our input data are extracted from catalog, consists longitude and latitude of past event between 1980-2015 with magnitude larger or equal to 4.5. It has concluded node D1 is candidate for big earthquakes in comparison with other nodes and other nodes are in lower levels of this potential.

The Effect of the Industrial Activities on Air Pollution at Baiji and Its Surrounding Areas, Iraq

The polluters of total suspended particles (TSP) and some heavy metals (Cd, Co, and Ni) concentrations were studied in the areas of Al-Fatha, Al-Alam and Baiji, Iraq. These concentrations were measured for selected 22 sample locations for two periods, January and July 2013. The analyzed values of (TSP) and (Cd) exceeded the limits of Iraqi National and the World Health Organization (WHO) for the two periods. Also, (Ni) values exceeded the limits for July only, while (Co) values were under the limits for the two periods. The difference between the two periods reflects the effect of the wind speed and direction, rainfall, and the intensity of the dust storms during the two months, respectively. GIS technique makes optimal predictions possible by examining the relationships between all the sample points and producing a continuous surface of polluter’s concentration. Therefore, GIS was used to produce predictions and probabilities maps for the critical polluter values in the study area.

Mem Brain: An Easy-to-Use Online Webserver for Transmembrane Protein Structure Prediction

Membrane proteins are an important kind of proteins embedded in the membranes of cells and play crucial roles in living organisms, such as ion channels,transporters, receptors. Because it is difficult to determinate the membrane protein's structure by wet-lab experiments,accurate and fast amino acid sequence-based computational methods are highly desired. In this paper, we report an online prediction tool called Mem Brain, whose input is the amino acid sequence. Mem Brain consists of specialized modules for predicting transmembrane helices, residue–residue contacts and relative accessible surface area of a-helical membrane proteins. Mem Brain achieves aprediction accuracy of 97.9% of ATMH, 87.1% of AP,3.2 ± 3.0 of N-score, 3.1 ± 2.8 of C-score. Mem BrainContact obtains 62%/64.1% prediction accuracy on training and independent dataset on top L/5 contact prediction,respectively. And Mem Brain-Rasa achieves Pearson correlation coefficient of 0.733 and its mean absolute error of13.593. These prediction results provide valuable hints for revealing the structure and function of membrane proteins.Mem Brain web server is free for academic use and available at www.csbio.sjtu.edu.cn/bioinf/Mem Brain/.

Soil polygon disaggregation through similarity-based prediction with legacy pedons

Conventional soil maps generally contain one or more soil types within a single soil polygon.But their geographic locations within the polygon are not specified.This restricts current applications of the maps in site-specific agricultural management and environmental modelling.We examined the utility of legacy pedon data for disaggregating soil polygons and the effectiveness of similarity-based prediction for making use of the under-or over-sampled legacy pedon data for the disaggregation.The method consisted of three steps.First,environmental similarities between the pedon sites and each location were computed based on soil formative environmental factors.Second,according to soil types of the pedon sites,the similarities were aggregated to derive similarity distribution for each soil type.Third,a hardening process was performed on the maps to allocate candidate soil types within the polygons.The study was conducted at the soil subgroup level in a semi-arid area situated in Manitoba,Canada.Based on 186 independent pedon sites,the evaluation of the disaggregated map of soil subgroups showed an overall accuracy of 67% and a Kappa statistic of 0.62.The map represented a better spatial pattern of soil subgroups in both detail and accuracy compared to a dominant soil subgroup map,which was commonly used in practice.Incorrect predictions mainly occurred in the agricultural plain area and the soil subgroups that are very similar in taxonomy,indicating that new environmental covariates need to be developed.We concluded that the combination of legacy pedon data with similarity-based prediction is an effective solution for soil polygon disaggregation.

Mapping prediction with recurrent neural networks for future LISP enabled networks

Locator/identifier separation paradigm(LISP)is an emerging Internet architecture evolution trend that decouples the identifier and location of an entity attached to the Internet.Due to its flexibility,LISP has seen its application in various fields such as mobile edge computing,and V2X networks.However,LISP relies on a DNS-like mapping system to associate identifiers and locations before connection establishment.Such a procedure incurs an extra latency overhead and thus hinders the adoption of LISP in delay-sensitive use cases.In this paper,we propose a novel RNN-based mapping prediction scheme to boost the performance of the LISP mapping resolution,by modeling the mapping procedure as a time series prediction problem.The key idea is to predict the mapping data regarding services to be utilized by users in edge networks administered by xTRs and proactively cache the mapping information within xTRs in advance.We compare our approach with several baseline methods,and the experiment results show a 30.02%performance gain in LISP cache hit ratio and 55.6%delay reduction compared with the case without mapping prediction scheme.This work preliminarily proves the potential of the approach in promoting lowlatency LISP-based use cases.

Mapping high resolution National Soil Information Grids of China

Soil spatial information has traditionally been presented as polygon maps at coarse scales. Solving global and local issues, including food security, water regulation, land degradation, and climate change requires higher quality, more consistent and detailed soil information. Accurate prediction of soil variation over large and complex areas with limited samples remains a challenge, which is especially significant for China due to its vast land area which contains the most diverse soil landscapes in the world. Here, we integrated predictive soil mapping paradigm with adaptive depth function fitting, state-of-the-art ensemble machine learning and high-resolution soil-forming environment characterization in a highperformance parallel computing environment to generate 90-m resolution national gridded maps of nine soil properties(pH, organic carbon, nitrogen, phosphorus, potassium, cation exchange capacity, bulk density, coarse fragments, and thickness) at multiple depths across China. This was based on approximately5000 representative soil profiles collected in a recent national soil survey and a suite of detailed covariates to characterize soil-forming environments. The predictive accuracy ranged from very good to moderate(Model Efficiency Coefficients from 0.71 to 0.36) at 0–5 cm. The predictive accuracy for most soil properties declined with depth. Compared with previous soil maps, we achieved significantly more detailed and accurate predictions which could well represent soil variations across the territory and are a significant contribution to the GlobalSoilMap.net project. The relative importance of soil-forming factors in the predictions varied by specific soil property and depth, suggesting the complexity and non-stationarity of comprehensive multi-factor interactions in the process of soil development.

A biogeographic map of soil bacterial communities in wheats field of the North China Plain

The vast diversity of soil bacteria provides essential ecosystem services that support agricultural production.Variation in the diversity and composition of soil biota may have predictive values for soil nutrient cycling and resilience of ecosystem services,thus providing valuable insights to improve food production.The North China Plain(NCP)is one of the world’s key agricultural regions,supplying more than 50% of the cereal consumed in Asia.However,it is unknown whether soil microbial diversity is predictable across the NCP.Using the MiSeq Illumina platform,we examined bacterial community variation in relation to spatial and environmental factors from 243 soils in wheat-maize double cropping rotation fields across the NCP,which cover nearly 0.3 million km^(2).Based on observed bacterial communities and their relationships with environmental factors,we generated a map of bacterial communities across the NCP.The highest bacterial diversity was found in the middle part of the NCP,with most of the variation in diversity attributable to differences in the community similarity of Actinobacteria and Alphaproteobacteria.These findings provide important baseline information for analyzing the relationships between microbial community,soil functionality and crop yields.

Mapping soil erodibility in southeast China at 250 m resolution:Using environmental variables and random forest regression with limited samples

Soil erodibility(K factor)mapping has been accomplished mainly by soil map-linked or geo-statistical interpolation.However,the resulting maps usually have coarse spatial resolution at a regional scale.The objectives of this study were a)to map the K factors using a set of environmental variables and random forest(RF)model,and b)to identify the important environmental variables in the predictive mapping on a regional scale.We collected 101 surface soil samples across southeast China in the summer of 2019.For each sample,we measured the particle size distribution and organic matter content,and calculated the K factors using the nomograph equation.The hyperparameters of RF were optimized through 5-fold cross validation(m_(ay)=2,n_(tree)=500,p=63),and a digital map with 250 m resolution was generated for the K factor.The lower and upper limits of a 90% prediction interval were also pro-duced for uncertainty analysis.It was found that the important environmental variables for the K factor prediction were relief,climate,land surface temperature and vegetation indexes.Since the existing K factor map has an average polygonal area of 6.8 km^(2),our approach dramatically improves the spatial resolution of the K factor to 0.0625 km^(2).The new method captures more distinct differences in spatial details,and the spatial distribution of the K factor derived from RF prediction followed a similar pattern with kriging interpolation.This suggests the presented approach in this study is effective for mapping the K factor with limited sampling data.

Do model choice and sample ratios separately or simultaneously influence soil organic matter prediction?

This study was performed to examine the separate and simultaneous influence of predictive models’choice alongside sample ratios selection in soil organic matter(SOM).The research was carried out in northern Morocco,characterized by relatively cold weather and diverse geological conditions.The dataset herein used accounted for 1591 soil samples,which were randomly split into the following ratios:10%(∼150 sample ratio),20%(∼250 sample ratio),35%(∼450 sample ratio),50%(∼600 sample ratio)and 95%(∼1200 sample ratio).Models herein involved were ordinary kriging(OK),regression kriging(RK),multiple linear regression(MLR),random forest(RF),quantile regression forest(QRF),Gaussian process regression(GPR)and an ensemble model.The findings in the study showed that the accuracy of SOM prediction is sensitive to both predictive models and sample ratios.OK combined with 95%sample ratio performed equally to RF in conjunction with all the sample ratios,as the latter did not show much sensitivity to sample ratios.ANOVA results revealed that RF with a∼10%sample ratio could also be optimum for predicting SOM in the study area.In conclusion,the findings herein reported could be instrumental for producing cost-effective detailed and accurate spatial estimation of SOM in other sites.Furthermore,they could serve as a baseline study for future research in the region or elsewhere.Therefore,we recommend conducting series of simulation of all possible combinations between various predictive models and sample ratios as a preliminary step in soil organic matter prediction.

Effect of thermo-mechanical processing parameters on the dynamic restoration mechanism in an Mg-4Y-2Nd-1Sm-0.5Zr alloy during hot compression

Microstructure evolution and dynamic restoration mechanism of solution-treated Mg-4Y-2Nd-1Sm-0.5Zr alloy have been studied under three TMP parameters consisting of deformation temperatures(350-500℃),strain rates(0.01-5 s^(-1)),and strains(0.2,0.4,and 0.8).A strong dynamic softening is observed in all stress-strain curves,even at higher strain rates(1 and 5 s^(-1))due to an adiabatic heating effect.Various stress-strain curves are applied to construct a processing map and develop an Arrhenius-type constitutive equation.With the prediction of the processing map,an optimal processing domain has been determined to be the temperature range 450-500℃and strain rate range 0.01-0.1 s^(-1)at a strain of 0.8.The volume fraction of DRX grains is the largest in the corresponding domain of high temperature and low strain rate.For the effect of TMP parameters on the dynamic restoration,the discontinuous dynamic recrystallization(DDRX)and continuous DRX(CDRX)synergistic effect occur throughout the whole process at high temperature and high strain rate.In terms of high temperature and low strain rate,DDRX characteristics at a low strain and then the DDRX+CDRX synergistic effect is observed at a higher strain.Although the DRX process is weak at low temperature and low strain rate,deformation twins have occurred and provided nucleation sites for DRX grains.