Traditional Datasets Research Articles

Investigating the Impact of Human Activity on Land Use/Cover Change in China’s Lijiang River Basin from the Perspective of Flow and Type of Population

Exploring how human activity impacts land use/cover change (LUCC) is a hot research topic in the field of geography and sustainability management. Researchers have primarily used socioeconomic variables to measure human activity. However, the human activity indexes mainly based on socioeconomic variables have a spatial resolution that is coarser than traditional LUCC datasets, which hinders a deep and comprehensive analysis. In view of these problems, we selected China’s Lijiang River Basin as our study area and proposed the use of GPS trajectory data for analyzing the impact of human activity on LUCC from two perspectives: (1) Type of population: we used the kernel density estimation method to extract the spatial distribution of activity intensity of local residents and tourists, investigated their correlation with the LUCC result, and found these two populations have different impacts on each land cover; (2) Flow of population: we used the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and a network analysis method to build a flow network of population from raw trajectories, conducted regression analysis with LUCC, and found that the flow of population is an important factor driving LUCC and is sometimes a more important factor than the static distribution of the population. Experimental results validated that the proposed method can be used to uncover the impact mechanism of human activity on LUCC at fine-grained scales and provide more accurate planning and instructions for sustainability management.

Discrete and morphometric traits reveal contrasting patterns and processes in the macroevolutionary history of a clade of scorpions.

Many palaeontological studies have investigated the evolution of entire body plans, generally relying on discrete character-taxon matrices. In contrast, macroevolutionary studies performed by neontologists have mostly focused on morphometric traits. Although these data types are very different, some studies have suggested that they capture common patterns. Nonetheless, the tests employed to support this claim have not explicitly incorporated a phylogenetic framework and may therefore be susceptible to confounding effects due to the presence of common phylogenetic structure. We address this question using the scorpion genus Brachistosternus Pocock 1893 as case study. We make use of a time-calibrated multilocus molecular phylogeny, and compile discrete and traditional morphometric data sets, both capturing the overall morphology of the organisms. We find that morphospaces derived from these matrices are significantly different, and that the degree of discordance cannot be replicated by simulations of random character evolution. Moreover, we find strong support for contrasting modes of evolution, with discrete characters being congruent with an 'early burst' scenario whereas morphometric traits suggest species-specific adaptations to have driven morphological evolution. The inferred macroevolutionary dynamics are therefore contingent on the choice of character type. Finally, we confirm that metrics of correlation fail to detect these profound differences given common phylogenetic structure in both data sets, and that methods incorporating a phylogenetic framework and accounting for expected covariance should be favoured.

BFSI-B: An improved K-hop graph reachability queries for cyber-physical systems

Cyber-physical systems, encompass the real physical space and virtual cyber space for providing advanced service for humans, which together, are also namely Internet of Things. The complex and large scale relationships among nodes contain the potential information of user, which is required and essential for providing high quality personalized service. Graph is used to represent, make fusion and process the relationships data, which has been used in many domains with traditional small data sets. K-hop reachability query answering in graphs is seeing a growing number of applications, such as ranked keyword search in databases, social networking, ontology reasoning and bioinformatics. Currently, techniques for efficient evaluation of such queries are based on vertex-cover technology. These methods suffer from a lack of scalability; that is, they cannot be applied to very large graphs. To solve these problems, we propose the compound-index method, namely BFSI-B, which uses the special index to decrease the k-hop reachability query time and improve pruning efficiency. The theoretical analysis and experiment results demonstrate that our method has a smaller index size and a faster query time than the k-reach method, in both dense graphs and very large graphs.

Multi‐proxy approach to palaeoenvironmental modelling: the English Lower Cretaceous Weald Basin

Palaeoenvironmental reconstructions of the Wealden (Lower Cretaceous) in southeast England have been previously based mainly on field and fossil data. This study presents new interpretations of multi‐proxy data sets such as sandstone petrography, elemental geochemistry and sideritic ironstones, supplemented by outcrop and clay mineralogical data. The dominance of quartz arenites, kaolinite and thorium and the presence of Zr in the sediments suggest that they were sourced mainly from granitic and/or gneissic rocks. Materials from metamorphic sources are secondary in abundance. The mineral and textural maturity of the sediments coupled with inferred short transport distances suggests reworking from older sources. Palaeoclimatic conditions in the source areas were generally warm and humid in nature, which supported an intensive weathering regime. The nature of the sediments suggests they may have been sourced directly or indirectly from a stable craton. The lithology, facies, sedimentary structures, architecture and gamma ray data confirm the freshwater origin of the sediments. Redox‐sensitive trace elements such as Mo, U, V and Co suggest anoxic conditions in the basin. The results presented in this paper reinforce the importance of integrating multi‐proxy data sets for interpreting the palaeoenvironmental conditions of sedimentary basins: when traditional data sets such as field and fossil data are not readily available or insufficient to enable adequate interpretations, the integration of petrographic, mineralogical and geochemical data sets may prove to be useful in similar palaeoenvironmental studies. Copyright © 2017 John Wiley & Sons, Ltd.

Front face fluorescence spectroscopy enables rapid differentiation of fresh and frozen-thawed sea bass (Dicentrarchus labrax) fillets

The aim of this study was to evaluate the potential of using front face fluorescence spectroscopy (FFFS) to discriminate fresh refrigerated (up to 13 days at 4 °C) sea bass (Dicentrarchus labrax) fillets (F group) from: i) frozen-thawed (3 months at −18 °C), then refrigerated up to 9 days fillets at 4 °C (F-T R group), and ii) refrigerated (up to 9 days at 4 °C), then frozen-thawed (3 months at −18 °C) fillets (R F-T group). The evolution of emission fluorescence spectra after excitation set at 290, 340, and 380 nm, and excitation spectra after emission set at 410 nm were acquired throughout storage. Some traditional measurements based on color, textural, and chemical parameters were performed on the same fillets. Canonical correlation analysis (CCA), applied to the traditional and fluorescence data sets, showed high correlations between the two data sets, since squared canonical coefficients were found higher than 0.8 for canonical variates 1 and 2. This was confirmed by the factorial discriminant analysis (FDA) applied to the concatenated emission spectra recorded after excitation sets at 340 and 380 nm since 72 out of 78 samples were correctly classified. Finally, a multi-block analysis method based on common components and specific weights analysis (CCSWA) was applied to the whole data sets. A reliable discrimination between fresh from frozen/thawed fish was found suggesting that FFFS could be used as a promising tool in routine inspection for differentiating between fresh and frozen-thawed fish.

Real-Time Riders: A First Look at User Interaction Data from the Back End of a Transit and Shared Mobility Smartphone App

A fundamental component of transit planning is understanding passenger travel patterns. However, traditional data sources used to study transit travel have some noteworthy drawbacks. For example, manual collection of travel surveys can be expensive, and data sets from automated fare collection systems often include only one transit system and do not capture multimodal trips (e.g., access and egress mode). New data sources from smartphone applications offer the opportunity to study transit travel patterns across multiple metropolitan regions and transit operators at little to no cost. Moreover, some smartphone applications integrate other shared mobility services, such as bikesharing, carsharing, and ride-hailing, which can provide a multimodal perspective not easily captured in traditional data sets. The objective of this research was to take a first look at an emerging data source: back-end data from user interactions with a smartphone application. The specific data set used in this paper was from a widely used smartphone application called Transit that provides real-time information about public transit and shared mobility services. Visualizations of individuals’ interactions with the Transit app were created to demonstrate three unique aspects of this data set: the ability to capture multicity transit travel, the ability to capture multiagency transit travel, and the ability to capture multimodal travel, such as the use of bikeshare to access transit. This data set was then qualitatively compared with traditional transit data sources, including travel surveys and automated fare collection data. The findings suggest that the data set has potential advantages over traditional data sources and could help transit planners better understand how passengers travel.

Hidden Markov Models in Population Genomics.

With the advent of sequencing techniques population genomics took a major shift. The structure of data sets has evolved from a sample of a few loci in the genome, sequenced in dozens of individuals, to collections of complete genomes, virtually comprising all available loci. Initially sequenced in a few individuals, such genomic data sets are now reaching and even exceeding the size of traditional data sets in the number of haplotypes sequenced. Because all loci in a genome are not independent, this evolution of data sets is mirrored by a methodological change. The evolutionary processes that generate the observed sequences are now modeled spatially along genomes whereas it was previously described temporally (either in a forward or backward manner). Although the spatial process of sequence evolution is complex, approximations to the model feature Markovian properties, permitting efficient inference. In this chapter, we introduce these recent developments that enable the modeling of the evolutionary history of a sample of several individual genomes. Such models assume the occurrence of meiotic recombination, and therefore, to date, they are dedicated to the analysis of eukaryotic species.

Attention shift-based multiple saliency object segmentation

Object segmentation is an important but highly challenging problem in computer vision and image processing. An attention shift-based multiple saliency object segmentation model, called ASMSO, is introduced. The proposed ASMSO could produce a pool of potential object regions for each saliency object and be applicable to multiple saliency object segmentation. The potential object regions are produced by combing the methods of gPb-owt-ucm and min-cut graph, whereas the saliency objects are detected by a visual attention model with an attention shift mechanism. In order to deal with various scenes, the model attention shift-based multiple saliency object segmentation (ASMSO) contains different features which include not only traditional features, such as color, uniform, and texture, but also a new position feature originating from proximity of Gestalt theory. Experiments on the training set of PASCAL VOC2012 segmentation dataset not only show that traditional color feature and the proposed position feature work much better than features of texture and uniformity, but also prove that ASMSO is suitable for multiple object segmentation. In addition, experiments on a traditional saliency dataset show that ASMSO could also be applied to traditional saliency object segmentation and performs much better than the state-of-the-art method.

Towards Systematic Parallelization of Graph Transformations Over Pregel

Graphs can be used to model many kinds of data, from traditional datasets to social networks or semi-structured datasets. To process large graphs, many systems have been proposed. The Pregel progra...

Seismic modelling of fault zones

While faults have long been known as primary pathways for fluid migration in sedimentary basins, recent work highlights the importance of fault zone internal architecture, lateral variation, transmissivity, and impact on migration and trapping. The impacts of fault zone architecture and properties on seismic images are investigated to facilitate accurately mapped fault zones, and to predict subseismic flow properties and sealing potential. A wedge-type fault model with a main fault and a synthetic fault displacing a typical North West Shelf siliciclastic succession is used to replicate the geometrical components of a seismic-scale fault. Elastic properties are derived from rock physics models, which are used in a 2D elastic modelling algorithm to produce realistic marine seismic acquisition geometry. These data were subsequently input into a 2D prestack (one-way wave-equation) migration code to produce an interpretable seismic image. Base-case elastic properties are systematically varied; modelling focuses on gouge properties, fractured fault zone material, the sandstone Vp/Vs relationship, and shale-sand velocity contrast. The workflow from geological model building to elastic property substitution and forward seismic modelling is extremely quick and versatile, allowing testing of a wide range of scenarios. So far this approach has yielded valuable insights into internal fault property prediction and interpretation of the fault zone in traditional post-stack seismic datasets. Implications for processing workflow and attenuation of fault shadows are also expected.

Phylogenomic methods outperform traditional multi-locus approaches in resolving deep evolutionary history: a case study of formicine ants.

BackgroundUltraconserved elements (UCEs) have been successfully used in phylogenomics for a variety of taxa, but their power in phylogenetic inference has yet to be extensively compared with that of traditional Sanger sequencing data sets. Moreover, UCE data on invertebrates, including insects, are sparse. We compared the phylogenetic informativeness of 959 UCE loci with a multi-locus data set of ten nuclear markers obtained via Sanger sequencing, testing the ability of these two types of data to resolve and date the evolutionary history of the second most species-rich subfamily of ants in the world, the Formicinae.ResultsPhylogenetic analyses show that UCEs are superior in resolving ancient and shallow relationships in formicine ants, demonstrated by increased node support and a more resolved phylogeny. Phylogenetic informativeness metrics indicate a twofold improvement relative to the 10-gene data matrix generated from the identical set of taxa. We were able to significantly improve formicine classification based on our comprehensive UCE phylogeny. Our divergence age estimations, using both UCE and Sanger data, indicate that crown-group Formicinae are older (104–117 Ma) than previously suggested. Biogeographic analyses infer that the diversification of the subfamily has occurred on all continents with no particular hub of cladogenesis.ConclusionsWe found UCEs to be far superior to the multi-locus data set in estimating formicine relationships. The early history of the clade remains uncertain due to ancient rapid divergence events that are unresolvable even with our genomic-scale data, although this might be largely an effect of several problematic taxa subtended by long branches. Our comparison of divergence ages from both Sanger and UCE data demonstrates the effectiveness of UCEs for dating analyses. This comparative study highlights both the promise and limitations of UCEs for insect phylogenomics, and will prove useful to the growing number of evolutionary biologists considering the transition from Sanger to next-generation sequencing approaches.Electronic supplementary materialThe online version of this article (doi:10.1186/s12862-015-0552-5) contains supplementary material, which is available to authorized users.

Towards Harnessing Phone Messages and Telephone Conversations for Prediction of Food Crisis

Food insecurity is a global challenge affecting millions of people especially those from least developed regions. Famine predictions are being carried out to estimate when shortage of food is most likely to happen. The traditional data sets such as house hold information, price trends, crop production trends and biophysical data used for predicting food insecurity are both labor intensive and expensive to acquire. Current trends are towards harnessing big data to study various phenomena such sentiment analysis and stock markets. Big data is said to be easier to obtain than traditional datasets. This study shows that phone messages archives and telephone conversations as big datasets are potential for predicting food crisis. This is timely with the current situation of massive penetration of mobile technology and the necessary data can be gathered to foster studies such as this. Computation techniques such as Naïve Bayes, Artificial Networks and Support Vector Machines are prospective candidates in this strategy. If the strategy is to work in a nation like Uganda, areas of concern have been highlighted. Future work points at exploring this approach experimentally.

Genome-wide ultraconserved elements exhibit higher phylogenetic informativeness than traditional gene markers in percomorph fishes

Ultraconserved elements (UCEs) have become popular markers in phylogenomic studies because of their cost effectiveness and their potential to resolve problematic phylogenetic relationships. Although UCE datasets typically contain a much larger number of loci and sites than more traditional datasets of PCR-amplified, single-copy, protein coding genes, a fraction of UCE sites are expected to be part of a nearly invariant core, and the relative performance of UCE datasets versus protein coding gene datasets is poorly understood. Here we use phylogenetic informativeness (PI) to compare the resolving power of multi-locus and UCE datasets in a sample of percomorph fishes with sequenced genomes (genome-enabled). We compare three data sets: UCE core regions, flanking sequence adjacent to the UCE core and a set of ten protein coding genes commonly used in fish systematics. We found the net informativeness of UCE core and flank regions to be roughly ten-fold and 100-fold more informative than that of the protein coding genes. On a per locus basis UCEs and protein coding genes exhibited similar levels of phylogenetic informativeness. Our results suggest that UCEs offer enormous potential for resolving relationships across the percomorph tree of life.

Daily minimum and maximum surface air temperatures from geostationary satellite data

AbstractObservations of daily minimum and maximum land air temperatures, Tmin and Tmax, have traditionally been obtained through in situ observations at meteorological stations. While the station network is extensive, many land masses are poorly observed. Moreover, observations at stations are “point” observations and may not be representative of air temperatures at neighboring locations. Satellites provide the means to observe surface skin temperatures at spatial scales of tens of meters to kilometers. But although skin and near‐surface air temperatures may be strongly coupled, the two quantities can differ by several degrees over land, where the magnitude of the difference is variable in both space and time. This study describes a method for estimating daily Tmin and Tmax at the pixel scale using geostationary satellite data, providing spatially detailed observations for areas unobserved in situ. A dynamic multiple linear regression model is developed using daily minimum and maximum land surface temperature (LSTmin and LSTmax), fraction of vegetation, distance from coast, latitude, urban fraction, and elevation as predictors. The method is demonstrated over Europe for 2012–2013; evaluation with collocated station observations indicates a mean satellite‐minus‐station bias of 0.0 to 0.5°C with root‐mean‐square difference of 2.3 to 2.7°C. The data derived here are not designed to replace traditional gridded station air temperature data sets, but to augment them. Satellite surface temperature data usually have larger uncertainties than in situ data sets, but they can offer spatial detail and coverage that the latter may not provide.

A Contribution to the Study of Ensemble of Self-Organizing Maps

This study presents a factorial experiment to investigate the ensemble of Kohonen Self-Organizing Maps. Clusters Validity Indexes and the Mean Square Quantization Error were used as a criterion for fusing Kohonen Maps, through three different equations and four approaches. Computational simulations were performed with traditional dataset, including those with high dimensionality, not linearly separable classes, Gaussian mixtures, almost touching clusters, and unbalanced classes, from the UCI Machine Learning Repository and from Fundamental Clustering Problems Suite, with variations in map size, number of ensemble components, and the percentage of dataset bagging. The proposed method achieves a better classification than a single Kohonen Map and we applied the Wilcoxon Signed Rank Test to evidence its effectiveness.

Improving Video Streams Summarization Using Synthetic Noisy Video Data

For monitoring public domains, surveillance camera systems are used. Reviewing and processing any subsequences from large amount of raw video streams is time and space consuming. Many efficient approaches of video summarization were proposed to reduce the amount of irrelevant information. Most of these approaches do not take into consideration the illumination or lighting changes that cause noise in video sequences. In this work, video summarization algorithm for video streams has been proposed using Histogram of Oriented Gradient and Correlation coefficients techniques. This algorithm has been applied on the proposed multi-model dataset which is created by combining the original data and the dynamic synthetic data. This dynamic data is proposed using Random Number Generator function. Experiments on this dataset showed the effectiveness of the proposed algorithm compared with traditional dataset.

Random Subspace Learning Approach to High-Dimensional Outliers Detection

We introduce and develop a novel approach to outlier detection based on adaptation of random subspace learning. Our proposed method handles both high-dimension low-sample size and traditional low-dimensional high-sample size datasets. Essentially, we avoid the computational bottleneck of techniques like Minimum Covariance Determinant (MCD) by computing the needed determinants and associated measures in much lower dimensional subspaces. Both theoretical and computational development of our approach reveal that it is computationally more efficient than the regularized methods in high-dimensional low-sample size, and often competes favorably with existing methods as far as the percentage of correct outlier detection are concerned.

Next-Generation AUC: Analysis of Multiwavelength Analytical Ultracentrifugation Data.

We describe important advances in methodologies for the analysis of multiwavelength data. In contrast to the Beckman-Coulter XL-A/I ultraviolet-visible light detector, multiwavelength detection is able to simultaneously collect sedimentation data for a large wavelength range in a single experiment. The additional dimension increases the data density by orders of magnitude, posing new challenges for data analysis and management. The additional data not only improve the statistics of the measurement but also provide new information for spectral characterization, which complements the hydrodynamic information. New data analysis and management approaches were integrated into the UltraScan software to address these challenges. In this chapter, we describe the enhancements and benefits realized by multiwavelength analysis and compare the results to those obtained from the traditional single-wavelength detector. We illustrate the advances offered by the new instruments by comparing results from mixtures that contain different ratios of protein and DNA samples, representing analytes with distinct spectral and hydrodynamic properties. For the first time, we demonstrate that the spectral dimension not only adds valuable detail, but when spectral properties are known, individual components with distinct spectral properties measured in a mixture by the multiwavelength system can be clearly separated and decomposed into traditional datasets for each of the spectrally distinct components, even when their sedimentation coefficients are virtually identical.

A Distributed Hydrological Model Driven by Multi-Source Spatial Data and Its Application in the Ili River Basin of Central Asia

Hydrological simulation in ungauged regions is a popular topic in water resource and environmental research, and is also an important part of the international research initiative Predictions in Ungauged Basins (PUB). In this study, a multi-spatial data-based DTVGM (MS-DTVGM), combining multi-source spatial data (MS-spatial data) with the Distributed Time-Variant Gain Model (DTVGM), was built in order to reduce dependence on conventional observation, and was applied to the Ili River basin where traditional data sets are scarce. Because it utilizes MS-spatial data to measure precipitation, potential evapotranspiration, air temperature, vegetation parameters, and soil parameters, the model is driven purely by data from common platforms, thus overcoming the disadvantage of the large amounts of data typically required for distributed hydrological models. The inputs and simulation results were calibrated and validated using station or field observations. The results indicate that: 1) the MS-DTVGM is feasible in the Ili River basin; all model inputs can be acquired from multi-source spatial data and the key parameters are accurate; 2) the MS-DTVGM has good performance on a monthly time scale, and its simulation results can be used for a longer time-scale water resource analysis; and (3) daily runoff generation correlated strongly with snowmelt, the R2 is about 0.69 indicating that the latter is an important contributor to water resources and suggesting that a snowmelt module is indispensable this area. The potential of distributed models for hydrological simulation in data-scarce regions using MS-spatial data was clearly demonstrated.

Background knowledge based privacy metric model for online social networks

The data of online social network (OSN) is collected currently by the third party for various purposes. One of the problems in such practices is how to measure the privacy breach to assure users. The recent work on OSN privacy is mainly focus on privacy-preserving data publishing. However, the work on privacy metric is not systematic but mainly focus on the traditional datasets. Compared with the traditional datasets, the attribute types in OSN are more diverse and the tuple is relevant to each other. The retweet and comment make the graph character of OSN notably. Furthermore, the open application programming interfaces (APIs) and lower register barrier make OSN open environment, in which the background knowledge is more easily achieved by adversaries. This paper analyzes the background knowledge in OSN and discusses its characteristics in detail. Then a privacy metric model faces OSN background knowledge based on kernel regression is proposed. In particular, this model takes the joint attributes and link knowledge into consideration. The effect of different data distributions is discussed. The real world data set from weibo.com has been adopted. It is demonstrated that the privacy metric algorithm in this article is effective in OSN privacy evaluation. The prediction error is 30% lower than that of the work mentioned above