Distributed Localization Algorithm for Wireless Sensor Network Based on Multidimensional Scaling and the Shortest Path Distance Correction

Sensor localization is crucial for the configuration and applications of wireless sensor network (WSN). A novel distributed localization algorithm, MDS-DC was proposed for wireless sensor network based on multidimensional scaling (MDS) and the shortest path distance correction. In MDS-DC, several local positioning regions with reasonable distribution were firstly constructed by an adaptive search algorithm, which ensures the mergence between the local relative maps of the adjacent local position regions and can reduce the number of common nodes in the network. Then, based on the relationships between the estimated distances and actual distances of anchors, the distance estimation vectors of sensors around anchors were corrected in each local positioning region. During the computations of the local relative coordinates, an iterative process, which is the combination of classical MDS algorithm and SMACOF algorithm, was applied. Finally, the global relative positions or absolute positions of sensors were obtained through merging the relative maps of all local positioning regions. Simulation results show that MDS-DC has better performances in positioning precision, energy efficiency and robustness to range error, which can meet the requirements of applications for sensor localization in WSN.

Bayesian Multidimensional Scaling for Location Awareness in Hybrid-Internet of Underwater Things

Localization of sensor nodes in the internet of underwater things(IoUT)is of considerable significance due to its various applications,such as navigation,data tagging,and detection of underwater objects.Therefore,in this paper,we propose a hybrid Bayesian multidimensional scaling(BMDS)based localization technique that can work on a fully hybrid IoUT network where the nodes can communicate using either optical,magnetic induction,and acoustic technologies.These communication technologies are already used for communication in the underwater environment;however,lacking localization solutions.Optical and magnetic induction communication achieves higher data rates for short communication.On the contrary,acoustic waves provide a low data rate for long-range underwater communication.The proposed method collectively uses optical,magnetic induction,and acoustic communication-based ranging to estimate the underwater sensor nodes’final locations.Moreover,we also analyze the proposed scheme by deriving the hybrid Cramer-Rao lower bound(H-CRLB).Simulation results provide a complete comparative analysis of the proposed method with the literature.

Multi-Vehicle Cooperative Positioning Based on Edge-Computed Multidimensional Scaling

As the basis of location-based services(LBS),positioning is one of the most essential parts in intelligent transportation systems(ITS).Although global positioning system(GPS)has been widely used in vehicle positioning,it can not achieve lane level positioning accuracy.Motivated by the mature ranging technologies such as radar and ultra-wideband(UWB),several cooperative positioning(CP)methods have been proposed to enhance the accuracy and robustness of GPS.In this paper,we proposed a twostage CP algorithm that combines multidimensional scaling(MDS)and Procrustes analysis for vehicles with GPS information.Specifically,the optimized MDS based on the scaling by majorizing a complicated function(SMACOF)algorithm is first proposed to get the relative coordinates of vehicles which can tackle measurements of different error distributions,then Procrustes analysis is carried out to transform the relative coordinates of vehicles to their absolute coordinates based on GPS information.All the computations are performed at the mobile edge computing node(MECN)for the request of ultra-reliable and low latency communications(URLLC).Simulation results validate that the proposed algorithm can greatly improve the positioning accuracy and robustness for vehicles.

Proposal for Generation of the Three-Way Perceptual Map Using Non-metric Multidimensional Scaling with Clusters

The generation of a perceptual map via three-way multidimensional scaling allows analysts to see the separation of objects in Euclidean space. The MDSvarext method incorporates the objects＇ confidence regions in this analysis, allowing for statistical inference in the difference between objects, but the confidence regions that are generated are very large because of the inherent variability among the evaluators. One solution to this problem is cluster generation prior to the application of the MDSvarext method in order to obtain homogeneous subgroups and to achieve greater control of the variance. This work is relevant to studies of perception which usually evaluate the difference between objects or stimuli in the point of view of different people that judge this difference using several dimensions. This study investigated the possibility of using a K-means algorithm to generate subgroups before the MDSvarext method was applied, evaluating the process with two quality indicators, one Ex-Ante and one Ex-Post. The experiments were conducted based on simulation of judgment matrix of different objects in multiple dimensions being evaluated by several judges. In this experiment, the matrix used was a 10 objects, in 10 features, judged by 10 people. The results are promising as possible interpretations of the perceptual map and the indicators generated.

Hour-to-hour variability of the ionosphere: An application of the classical multidimensional scaling method

The classical multidimensional scaling(MDS) method is introduced and applied in the study of the hour-to-hour ionospheric variability based on the ionospheric fo F2 observed at three ionosonde stations in East-Asia in 2002 and 2007. Results from the matrix eigen decompositions indicate that the annual part of the ionospheric variation is large in middle latitude and solar maximum period(2002) while low in the low latitude and solar minimum period(2007). The connectivity maps of the hour-to-hour ionospheric variability based on MDS method show some common diurnal features. The ionospheric connectivity between adjacent hours near noon hours and near midnight hours is high. The ionospheric connectivity between adjacent hours near sunrise hours and near sunset hours is poor, especially for the sunrise hours. Also there are latitudinal and solar activity dependences in this kind of connectivity. These results revealed from the ionospheric connectivity maps are useful physically and in practice for the ionospheric forecasting on the hour-to-hour scale.

Evolutionary Pattern and Effect of Administrative Division Adjustment During Urbanization of China:Empirical Analysis on Multiple Scales

The adjustment of administrative divisions is one of the important factors guiding China's urbanization, which has profound economic and social effects for regional development. In this paper, we comprehensively describe the process of the adjustment of administrative divisions at provincial and municipal levels in China and summarize the effects on the basic structure and patterns of the spatial development. We quantitatively assess the effects on fields such as urbanization and social economy through the use of multidimensional scaling. The results show that: 1) Upgrading county to municipality(or city-governed district) is the main way of adjusting the administrative divisions. It is also an important factor in the spatial differentiation of interprovincial urbanization. China's population urbanization can be divided into four patterns including interprovincial migration, provincial migration, natural growth, and growth caused by the adjustment of administrative divisions, which is also the main reason for the increased Chinese urbanization rate at the provincial level. 2) Taking the city of Beijing as an example, we generalize five adjustment patterns made to administrative divisions: the set-up of sub-districts, the set-up of regional offices, the upgrading of townships to sub-districts, the upgrading of townships to towns, and the set-up of towns and the addition of new regional offices. We summarize the municipal urban spatial structure, including the sub-district office area in the central urban area, the regional office area in the new urban area, the mixed area of villages, towns, and sub-district offices in the suburb area, and the township area in the outer suburb area. 3) The adjustment of administrative divisions triggers a significant circulative accumulation effect, resulting in the spatial locking of population and industrial agglomeration. It affects the evolution of the urban spatial form and plays an important role in shaping the urban spatial structure to move to the characteristic of multicenter. In general, the adjustment of administrative divisions was an important factor affecting the inflated statistical level of urbanization and also an important driving force for the evolution of Chinese urban spatial organization structure.

Alpine Ecosystems of Northwest Yunnan,China:an Initial Assessment for Conservation

Implementing conservation actions on-the-ground is not a straightforward process,especially when faced with high scientific uncertainty due to limited available information. This is especially acute in regions of the world that harbor many unique species that have not been well studied,such as the alpine zone of the Hengduan Mountains of Northwest Yunnan (NWY),a global biodiversity hotspot and site of The Nature Conservancy’s Yunnan Great Rivers Project. We conducted a quantitative,but rapid regional-level assessment of the alpine flora across NWY to provide a broad-based understanding of local and regional patterns of the alpine flora,the first large-scale analysis of alpine biodiversity patterns in this region. Multivariate analyses were used to classify the major plant community types and link community patterns to habitat variables. Our analysis indicated that most species had small distributions and/or small population sizes. Strong patterns emerged with higher diversity in the more northern mountains,but beta diversity was high,averaging only 10% among sites. The ordinations indicated that elevation and geographic location were the dominant environ-mental gradients underlying the differences in the species composition among communities. The high beta diversity across the alpine of these mountains implies that conservation strategies ultimately will require the protection of large numbers of species over a large geographical area. However,prioritiza-tion should be given to areas where potential payoffs are greatest. Sites with high species richness also have a greater number of endemic species,and,by focusing efforts on these sites,conservation investments would be maximized by protecting the greatest number of unique species.

Nonlinear Dimensionality Reduction and Data Visualization:A Review

Dimensionality reduction and data visualization are useful and important processes in pattern recognition. Many techniques have been developed in the recent years. The self-organizing map （SOM） can be an efficient method for this purpose. This paper reviews recent advances in this area and related approaches such as multidimensional scaling （MDS）, nonlinear PC A, principal manifolds, as well as the connections of the SOM and its recent variant, the visualization induced SOM （ViSOM）, with these approaches. The SOM is shown to produce a quantized, qualitative scaling and while the ViSOM a quantitative or metric scaling and approximates principal curve/surface. The SOM can also be regarded as a generalized MDS to relate two metric spaces by forming a topological mapping between them. The relationships among various recently proposed techniques such as ViSOM, Isomap, LLE, and eigenmap are discussed and compared.

The key environmental factors driving the succession of phytoplankton functional groups in Hongfeng Reservoir, southwest China

Reservoirs are an important water source in many densely populated areas in southwest China.Phytoplankton play an essential role in maintaining the structure and function of reservoir ecosystems.Understanding the succession in phytoplankton communities and the factors driving it are essential for eff ective water quality management in drinking water reservoirs.In this study,water samples were collected monthly at the surface layers from March 2016 to December 2019 in Hongfeng Reservoir,southwest China.The relationship between functional group succession was analyzed based on nonmetric multidimensional scaling analysis(NMDS),redundancy analysis(RDA),succession rate,and other analysis methods.The results showed distinct shifts in the community structure of phytoplankton functional groups within study period.The Cyclotella sp.was dominant in 2016 and 2017,and Pseudanabaena limnetica was the dominant group in 2018 and 2019.It appears that the phytoplankton composition and biomass are closely related to the water temperature and nutrient status in this reservoir.The results clearly showed that the permanganate index(COD_(Mn))was the key factor of dramatic phytoplankton functional group succession,and the change in succession rates was closely caused by total nitrogen concentration(TN).Therefore,the succession pattern and key factors of Hongfeng Reservoir revealed in this study were important guidance for the management of drinking water reservoirs in southwest China.A reasonable limit on exogenous nutrient input should be a priority,especially in high water temperature period.

Grain size-dependent geochemical evidence reveals provenance and implications of aeolian sands,Dinggye region,southern Tibet

Identifying geochemical characteristics of aeolian sands on the Qinghai-Tibet Plateau(QTP)is essential for understanding the relationship between earth surface processes and paleoclimatic fluctuations in the region.Here,we present new geochemical data which provides insight to the sedimentary environment of aeolian sands in the Dinggye region,southern Tibet.We sampled aeolian dune sands in a variety of settings,and determined grain size and concentration of major oxides and trace elements in the fine and coarse fractions.Results show that aeolian sediments are dominated by fine and medium sands,with a single-peaked frequency curve and a 3-stage probability cumulative curve.The fine and coarse fractions exhibit considerable heterogeneity in elemental concentrations and ratios and upper continental crust-normalized(UCC)distribution.The geochemical evidence indicates that wind dynamic sorting is responsible for the differentiation between fine and coarse fractions in different types of aeolian sand,rather than sediment provenance.Additionally,fine-fraction sediments are well dispersed and can be differentiated from the coarse fraction,suggesting that they contain more environmental information.Multidimensional scale(MDS)and principal component analysis(PCA)of commonly used tracer elements show that flood plain sediments are the sand source for mobile dunes and nebkhas,and lakeshore sediments are the sand source for climbing sand sheets.

Caries Patterns in the Primary Dentition: Cluster Analysis of a Sample of 5,169 Arizona Children 5-59 Months of Age

Aim Cluster analysis was conducted on data from 5,169 United States （U.S.） Arizona children, age＇s 5-59-months with the goal of delineating patterns of caries in the primary dentition of pre-school children without a priori pattern definitions. Methodology Cluster analyses were conducted using all data for children ages 0-4 years in aggregate： 1） for all subjects, and 2） for subjects without crowned restored teeth. Each of these two sets of analyses consisted of 8 differently specified cluster analyses as a validation procedure. Results The caries patterns identified from the clustering analysis are： 1） smooth surfaces （other than the maxillary incisor）, 2） maxillary incisor, 3） occlusal surfaces of first molars, and 4） pit and fissure surfaces of second molars. Conclusion The cluster analysis findings were consistent with results produced by multidimensional scaling. These cross-validated patterns may represent resulting disease conditions from different risks or the timing of various risk factor exposures. As such, the patterns may be useful case definitions for caries risk factor investigations in children under 60 months of age.

The Characteristic Patterns of Macrofaunal Fouling Assemblages in Nearshore Waters of the South China Sea

The spatio-temporal patterns of macrofaunal fouling assemblages were quantitatively investigated in the nearshore waters of the South China Sea.The work was undertaken by deploying seasonal panels at two sites(H-site,L-site) for one year,and the fouling communities on the panels were examined and analyzed.The results indicated that species composition of assemblages was obviously different between the two sites.At both sites the assemblages were characteristic with solitary dominant species throughout the year,with Amphibalanus reticulates dominating at H-site and Hydroides elegans at L-site.Shannon index and biomass of the assemblages varied with depth and season at both sites.At H-site the total biomass in summer and autumn were significantly higher than those in spring and winter,while at L-site the assemblage biomass also differed significantly among the four seasons,and the greatest biomass occurred at the depth of 2.0 m in winter.The abundance of all seasonal samples in non-metric multidimensional scaling was clustered as one group at L-site and three groups at H-site.The environmental factors were more likely to be related to the variation of fouling assemblages.Furthermore,it also suggests that in tropical seas the integrated adaptability would qualify a species for dominating a fouling assemblage despite its short life cycle,rather than the usually assumed only species with long life span.This study reveals the complexity and characteristic dynamics of macrofaunal fouling assemblages in the tropical habitats,and the results would provide valuable knowledge for biodiversity and antifouling research.

Mapping species assemblages of tropical forests at different hierarchical levels based on multivariate regression trees

Background: Vegetation distribution maps are of great significance for nature protection and management. In diverse tropical forests, accurate spatial mapping of vegetation types is challenging;the high species diversity and abundance of rare species challenge classification concepts, while remote sensing signals may not vary systematically with species composition, complicating the technical capability for delineating vegetation types in the landscape.Methods: We used a combination of field-based compositional data and their relations to environmental variables to predict the distribution of forest types in the Wuzhishan National Natural Reserve(WNNR), Hainan Island,China, using multivariate regression trees(MRT). The MRT was based on arboreal vegetation composition in 132plots of 20 m×20 m with a regular spacing of 1 km. Apart from the MRT, non-metric multidimensional scaling(NMDS) was used to evaluate vegetation-environment relationships.Results: The MRT model worked best when using 14 key environmental variables including topography, climate,latitude and soil, although the difference with the simpler model including only topographical variables was small. The full model classified the 132 plots into 3 vegetation types, 6 formation groups, 20 formations and 65associations at different hierarchical syntaxonomic levels. This model was the basis for forest vegetation maps for the WNNR. MRT and NMDS showed that elevation was the main driving force for the distribution of vegetation types and formation groups. Climate, latitude, and soil(especially available P), together with topographic variables, all influenced the distribution of formations and associations.Conclusions: While elevation determines forest-type distributions, lower-level syntaxonomic forest classes respond to the topographic diversity typical for mountains. Apart from providing the first detailed forest vegetation map for any part of WNNR, we show how, in spite of limitations, MRT with existing environmental data can be a useful method for mapping diverse and remote tropical forests.

Metrics analysis of tactile perceptual space based on improved NMDS for leather textures

To solve the fuzzy and unstable tactile similarity relationship between some sample points in the perception experiment,an improved non-metric multidimensional scaling(INMDS)is proposed in this paper.In view of the inconsistency of each sample s contribution,the maximum marginal decision when constructing the perception space to describe the tactile perception characteristics is also proposed.The corresponding constraints are set according to the degree of similarity,and controlling the relaxation variable factor is proposed to optimize the perception dimension and coordinate measurement.The effectiveness of the INMDS algorithm is verified by two perception experiments.The results show that compared with the metric multidimensional scaling(MDS)and non-metric multidimensional scaling(NMDS)algorithms,the perceptual space constructed by INMDS can more accurately reflect the difference relationship between different leather sample points perceived by people.Moreover,the relative position of sample points in the perceptual space is more consistent with subjective perception results.

Bibliometric analysis of researches of empowerment theory applied to nursing in China

Objective:To analysis the development trend and research focus of empowerment theory applied to Nursing in China.Methods:Literatures related to the objective were searched and collected from CNKI,WangFang,VIP and CBM,then Excel 2003 was used to setup the database and co-word matrix,SPSS 21.0 was utilized to make the visualized analysis by way of multivariate statistics analysis,cluster analysis and multidimensional scaling analysis.Results:Literatures with the number of 486 were selected out and 18 high frequency keywords were retrieved from 140 journals.Among the literatures,the first one was published in 2002,then a tremendous rising started since 2009,and reached the peak in 2017,mainly from the southern part of China,such as the province of Jiangsu,Guangdong,and Zhejiang.Regarding the content of the literatures,the research of intervention accounted for 61.32%,then the research of description came to the second at the ratio of 24.49%.What’s more,378(77.78%)were cited,154(31.69%)were funded.Conclusion:Nowadays,empowerment applied in the therapy of chronic disease is the focus and trend of the research of empowerment theory,and the psychological empowerment to nursing staff,as well as the constructed empowerment is going mature.In the future,more attention should be paid to the study and practice of empowerment theory,in order to vary the direction of research and enrich the theory.

On the Role of Security in the System of Personal Values:Are Conservatives Prioritizing Security?

Security is a value contained in most theories of personal values.Yet,while the relations among the most basic values are clear and reliable,the role of security in the system of values remains ambiguous.People striving for security are often also striving for tradition and conformity but sometimes they are focusing more on other values(such as health values,for example).Based on eight representative surveys(N=24,000)in several German cities between 1998 and 2022,the author shows that when measuring security without suggesting a particular meaning of this notion,security takes a relatively central position within the system of values and their components(shown by multidimensional scaling,MDS).People striving for security also emphasize the importance of law and order,working hard,and having a good family life as guiding principles in their lives.Conformity is not that important for them,and having an exciting life is even negatively correlated.Age has little impact on the MDS structure of values and their components,even though people exhibit substantial changes in the relative weights they assign to many values as they get older.

Effects of environmental factors and host characteristics on diversity and distribution of wood-rotting fungi of Mount Puliebadze,Nagaland

The study on the effects of environmental factors and host characteristics on diversity and distribution of wood-rotting fungi Mount Puliebadze,Nagaland was carried out for a period of two successive years(January 2015 to December 2016).A total of 46 wood-rotting fungi belonging to 16 families were identified.The occurrence of wood-rotting fungi demonstrated a decreasing trend with increase in elevations and correlation between the two variables showed a strong negative correlation with Pearson’s correlation coefficient(r)value of-0.993.The zone with lowest elevation(zone 1)comprised maximum number of species(25 species)whereas the zone with highest elevation(zone 4)comprised minimum number of species(05 species).Highest species similarity percentage(25.9%)was observed between zone 2 and 3.One way ANOVA showed significant variations between the occurrence of wood-rotting fungi with different seasons,light intensity,type of substrata and decay stage of wood.Shannon’s diversity index(H’)of zone 1 was highest(H’=3.073)and that of zone 4 was lowest(H’=1.242).

MIoT Based Skin Cancer Detection Using Bregman Recurrent Deep Learning

Mobile clouds are the most common medium for aggregating,storing,and analyzing data from the medical Internet of Things(MIoT).It is employed to monitor a patient’s essential health signs for earlier disease diagnosis and prediction.Among the various disease,skin cancer was the wide variety of cancer,as well as enhances the endurance rate.In recent years,many skin cancer classification systems using machine and deep learning models have been developed for classifying skin tumors,including malignant melanoma(MM)and other skin cancers.However,accurate cancer detection was not performed with minimum time consumption.In order to address these existing problems,a novel Multidimensional Bregman Divergencive Feature Scaling Based Cophenetic Piecewise Regression Recurrent Deep Learning Classification(MBDFS-CPRRDLC)technique is introduced for detecting cancer at an earlier stage.The MBDFS-CPRRDLC performs skin cancer detection using different layers such as input,hidden,and output for feature selection and classification.The patient information is composed of IoT.The patient information was stored in mobile clouds server for performing predictive analytics.The collected data are sent to the recurrent deep learning classifier.In the first hidden layer,the feature selection process is carried out using the Multidimensional Bregman Divergencive Feature Scaling technique to find the significant features for disease identification resulting in decreases time consumption.Followed by,the disease classification is carried out in the second hidden layer using cophenetic correlative piecewise regression for analyzing the testing and training data.This process is repeatedly performed until the error gets minimized.In this way,disease classification is accurately performed with higher accuracy.Experimental evaluation is carried out for factors namely Accuracy,precision,recall,F-measure,as well as cancer detection time,by the amount of patient data.The observed result confirms that the proposed MBDFS-CPRRDLC technique increases accuracy as well as lesser cancer detection time compared to the conventional approaches.

Effect of signal-to-noise ratio on the automatic clustering of X-ray diffraction patterns from combinatorial libraries

Hierarchical clustering algorithm has been applied to identify the X-ray diffraction(XRD)patterns from a high-throughput characterization of the combinatorial materials chips.As data quality is usually correlated with acquisition time,it is important to study the hierarchical clustering performance as a function of data quality in order to optimize the efficiency of high-throughput experiments.This work investigated the effects of signal-to-noise ratio on the performance of hier-archical clustering using 29 distance metrics for the XRD patterns from Fe−Co−Ni ternary combinatorial materials chip.It is found that the clustering accuracies evaluated by the F1 score only fluctuate slightly with signal-to-noise ratio varying from 15.5 to 22.3(dB)under the experimental condition.This suggests that although it may take 40-50 s to collect a visually high-quality diffraction pattern,the measurement time could be significantly reduced to as low as 4 s without substantial loss in phase identification accuracy by hierarchical clustering.Among the 29 distance metrics,Pearsonχ^(2)shows the highest mean F1 score of 0.77 and lowest standard deviation of 0.008.It shows that the distance matrixes calculated by Pearsonχ^(2)are mainly controlled by the XRD peak shifting characteristics and visualized by the metric multidimensional data scaling.

AN INEXACT SMOOTHING NEWTON METHOD FOR EUCLIDEAN DISTANCE MATRIX OPTIMIZATION UNDER ORDINAL CONSTRAINTS

When the coordinates of a set of points are known, the pairwise Euclidean distances among the points can be easily computed. Conversely, if the Euclidean distance matrix is given, a set of coordinates for those points can be computed through the well known classical Multi-Dimensional Scaling （MDS）. In this paper, we consider the case where some of the distances are far from being accurate （containing large noises or even missing）. In such a situation, the order of the known distances （i.e., some distances are larger than others） is valuable information that often yields far more accurate construction of the points than just using the magnitude of the known distances. The methods making use of the order information is collectively known as nonmetric MDS. A challenging computational issue among all existing nonmetric MDS methods is that there are often a large number of ordinal constraints. In this paper, we cast this problem as a matrix optimization problem with ordinal constraints. We then adapt an existing smoothing Newton method to our matrix problem. Extensive numerical results demonstrate the efficiency of the algorithm, which can potentially handle a very large number of ordinal constraints.