Mapping-based Approach Research Articles

Producer Mobility Support Schemes for Named Data Networking: A Survey

Mobile devices connectivity and data traffic growth requires scalable and efficient means of data distribution over the Internet. Thus, influenced the needs for upgrading or replacing the current Internet architecture to cater the situation as Named Data Networking (NDN) was proposed. NDN is clean-slate Internet architecture, proposed to replace IP with hierarchical named content that utilizes route aggregation to improve scalability and support mobility. Although, NDN provides supports for content consumer mobility with the help of catching capabilities, however, content producer faces many problems similar to mobility in IP architecture, such as, long handoff delay, unnecessary Interest packet losses and high bandwidth utilization. Hence, many concepts and schemes were proposed to address these problems. This paper reviewed and conceptually analyzed the schemes based on their fundamental design that broadly categorized into indirection-based approach, mapping-based approach, locator-based approach and control/data plane-based approach. In the review analysis, mapping-based approach schemes provide optimal path for packets delivery, high handoff delay Indirection-based and locator-based approach schemes provide normal handoff delay, but introduces tiangular routing path. The control/data plane-based approach schemes provide sub-optimal routing path and high handoff delay. The paper provided both strength and weakness of each scheme for further research.

A Co-Point Mapping-Based Approach to Drivable Area Detection for Self-Driving Cars

The randomness and complexity of urban traffic scenes make it a difficult task for self-driving cars to detect drivable areas. Inspired by human driving behaviors, we propose a novel method of drivable area detection for self-driving cars based on fusing pixel information from a monocular camera with spatial information from a light detection and ranging (LIDAR) scanner. Similar to the bijection of collineation, a new concept called co-point mapping, which is a bijection that maps points from the LIDAR scanner to points on the edge of the image segmentation, is introduced in the proposed method. Our method positions candidate drivable areas through self-learning models based on the initial drivable areas that are obtained by fusing obstacle information with superpixels. In addition, a fusion of four features is applied in order to achieve a more robust performance. In particular, a feature called drivable degree (DD) is proposed to characterize the drivable degree of the LIDAR points. After the initial drivable area is characterized by the features obtained through self-learning, a Bayesian framework is utilized to calculate the final probability map of the drivable area. Our approach introduces no common hypothesis and requires no training steps; yet it yields a state-of-art performance when tested on the ROAD-KITTI benchmark. Experimental results demonstrate that the proposed method is a general and efficient approach for detecting drivable area.

Structural insights into pharmacophore-assisted in silico identification of protein–protein interaction inhibitors for inhibition of human toll-like receptor 4 – myeloid differentiation factor-2 (hTLR4−MD-2) complex

Toll-like receptor 4 (TLR4) is a member of Toll-Like Receptors (TLRs) family that serves as a receptor for bacterial lipopolysaccharide (LPS). TLR4 alone cannot recognize LPS without aid of co-receptor myeloid differentiation factor-2 (MD-2). Binding of LPS with TLR4 forms a LPS−TLR4−MD-2 complex and directs downstream signaling for activation of immune response, inflammation and NF-κB activation. Activation of TLR4 signaling is associated with various pathophysiological consequences. Therefore, targeting protein–protein interaction (PPI) in TLR4−MD-2 complex formation could be an attractive therapeutic approach for targeting inflammatory disorders. The aim of present study was directed to identify small molecule PPI inhibitors (SMPPIIs) using pharmacophore mapping-based approach of computational drug discovery. Here, we had retrieved the information about the hot spot residues and their pharmacophoric features at both primary (TLR4−MD-2) and dimerization (MD-2−TLR4*) protein–protein interaction interfaces in TLR4−MD-2 homo-dimer complex using in silico methods. Promising candidates were identified after virtual screening, which may restrict TLR4−MD-2 protein–protein interaction. In silico off-target profiling over the virtually screened compounds revealed other possible molecular targets. Two of the virtually screened compounds (C11 and C15) were predicted to have an inhibitory concentration in μM range after HYDE assessment. Molecular dynamics simulation study performed for these two compounds in complex with target protein confirms the stability of the complex. After virtual high throughput screening we found selective hTLR4−MD-2 inhibitors, which may have therapeutic potential to target chronic inflammatory diseases.

A scenario simulation approach for sustainable mobility project evaluation based on fuzzy cognitive maps

Sustainability evaluation of new urban mobility projects is a challenging decision due to the presence of multiple objectives (social, economic, environmental), limited data availability, presence of multiple stakeholders, and specific context of each city. Scenario modeling and simulation is a useful tool to address such situations. In this paper, we present a fuzzy cognitive mapping-based scenario simulation approach for evaluating sustainable mobility projects. Linguistic assessments and fuzzy set theory are used to address the uncertainty arising from lack of quantitative data. The criteria for sustainability evaluation are obtained using fuzzy Delphi technique. A numerical application is provided for the city of Luxemburg. The strength of the proposed approach is the ability to aid in decision-making of new sustainable mobility project evaluation and selection under limited or no quantitative data availability. In addition, it is able to deal with multiple, co-related and conflicting criteria in evaluation.

Defining Determinism

The article puts forward a branching-style framework for the analysis of determinism and indeterminism of scientific theories, starting from the core idea that an indeterministic system is one whose present allows for more than one alternative possible future. We describe how a definition of determinism stated in terms of branching models supplements and improves current treatments of determinism of theories of physics. In these treatments, we identify three main approaches: one based on the study of (differential) equations, one based on mappings between temporal realizations, and one based on branching models. We first give an overview of these approaches and show that current orthodoxy advocates a combination of the mapping- and the equations-based approaches. After giving a detailed formal explication of a branching-based definition of determinism, we consider three concrete applications and end with a formal comparison of the branching- and the mapping-based approach. We conclude that the branching-based definition of determinism most usefully combines formal clarity, connection with an underlying philosophical notion of determinism, and relevance for the practical assessment of theories. 1. Introduction2. Determinism in Philosophy of Science: Three Approaches 2.1. Determinism: The core idea and how to spell it out2.2. The three approaches in more detail2.3. Representing indeterminism3. Orthodoxy: DMAP, with Invocations of DEQN4. Branching-Style Determinism (DBRN) 4.1. Models and realizations4.2. Faithfulness4.3. Two types of branching topologies5. Comparing the Approaches 5.1. Case studies5.2. Formal comparison of the DMAP and DBRN frameworks6. Conclusions

Complexity analysis of manufacturing service ecosystem: a mapping-based computational experiment approach

The trend of servitisation is increasingly affecting manufacturing enterprises. Traditional manufacturing enterprises cannot handle the related challenges of service innovation by themselves. Recently, manufacturing service ecosystem (MSE) has been proposed to support service innovation by facilitating collaboration. The construction and development of MSE need to handle a series of complexities, such as individual complexity, interaction complexity and ecological complexity. However, it is still very difficult to clearly identify the possible effect of various influence factors on MSE evolution, which is necessary analyse the complex dynamic interactive relationship among participants, so as to maintain the sustainable and healthy development of MSE. To change such a situation, this paper proposes a mapping-based computational experiment approach to analyse the evolution of MSE. This approach has three main parts, i.e. model construction of real world, model mapping of computational system and experiment evaluation of various factors of MSE evolution. By adopting the proposed approach, several case studies are conducted to investigate the possible effect of cooperation preference on the MSE evolution in various market environments. The results demonstrate that the proposed approach is effective.

Neural Skyline Filtering for Imbalance Features Classification

In the current digitalized era, large datasets play a vital role in features extractions, information processing, knowledge mining and management. Sometimes, existing mining approaches are not sufficient to handle large volume of datasets. Biological data processing also suffers for the same issue. In the present work, a classification process is carried out on large volume of exons and introns from a set of raw data. The proposed work is designed into two parts as pre-processing and mapping-based classification. For pre-processing, three filtering techniques have been used. However, these traditional filtering techniques face difficulties for large datasets due to the long required time during large data processing as well as the large required memory size. In this regard, a mapping-based neural skyline filtering approach is designed. Randomized algorithm performed the mapping for large volume of datasets based on objective function. The objective function determines the randomized size of the datasets according to the homogeneity. Around 200 million DNA base pairs have been used for experimental analysis. Experimental result shows that mapping centric filtering outperforms other filtering techniques during large data processing.

Networks Underpinning Symbiosis Revealed Through Cross-Species eQTL Mapping.

Organisms engage in extensive cross-species molecular dialog, yet the underlying molecular actors are known for only a few interactions. Many techniques have been designed to uncover genes involved in signaling between organisms. Typically, these focus on only one of the partners. We developed an expression quantitative trait locus (eQTL) mapping-based approach to identify cause-and-effect relationships between genes from two partners engaged in an interspecific interaction. We demonstrated the approach by assaying expression of 98 isogenic plants (Medicago truncatula), each inoculated with a genetically distinct line of the diploid parasitic nematode Meloidogyne hapla With this design, systematic differences in gene expression across host plants could be mapped to genetic polymorphisms of their infecting parasites. The effects of parasite genotypes on plant gene expression were often substantial, with up to 90-fold (P = 3.2 × 10-52) changes in expression levels caused by individual parasite loci. Mapped loci included a number of pleiotropic sites, including one 87-kb parasite locus that modulated expression of >60 host genes. The 213 host genes identified were substantially enriched for transcription factors. We distilled higher-order connections between polymorphisms and genes from both species via network inference. To replicate our results and test whether effects were conserved across a broader host range, we performed a confirmatory experiment using M. hapla-infected tomato. This revealed that homologous genes were similarly affected. Finally, to validate the broader utility of cross-species eQTL mapping, we applied the strategy to data from a Salmonella infection study, successfully identifying polymorphisms in the human genome affecting bacterial expression.

Correction of metal-induced susceptibility artifacts for functional MRI during deep brain stimulation

Functional magnetic resonance imaging (fMRI) is an emerging tool for investigating brain activation associated with, or modulated by, deep brain stimulation (DBS). However, DBS-fMRI generally suffers from severe susceptibility to artifacts in regions near the metallic stimulation electrodes, as well as near tissue/air boundaries of the brain. These result in strong intensity and geometric distortions along the phase-encoding (PE) (i.e., blipped) direction in gradient-echo echo-planar imaging (GE-EPI). Distortion presents a major challenge to conducting reliable data analysis and in interpreting the findings. A recent study showed that the point spread function (PSF) mapping-based reverse gradient approach has a potential to correct for distortions not only in spin-echo EPI, but also in GE-EPI acquired in both the forward and reverse PE directions. In this study, we adapted that approach in order to minimize severe metal-induced susceptibility artifacts for DBS-fMRI, and to evaluate the performance of the approach in a phantom study and a large animal DBS-fMRI study. The method combines the distortion-corrected GE-EPI pair with geometrically different intensity distortions due to the opposing encoding directions. The results demonstrate that the approach can minimize susceptibility artifacts that appear around the metallic electrodes, as well as in the regions near the tissue/air boundaries in the brain. We also demonstrated that an accurate geometric correction is important in improving BOLD contrast in the group dataset, especially in regions where strong susceptibility artifacts appear.

Comparison of molecular serotyping approaches of Streptococcus agalactiae from genomic sequences

BackgroundGroup B streptococcus (GBS) capsular polysaccharide is one of the major virulence factors underlying invasive GBS disease and a component of forthcoming vaccines. Serotype classification of GBS is based on the capsule polysaccharide of which ten variants are known to exist (Ia, Ib, II-IX). Current methods for GBS serotype assignment rely on latex agglutination or PCR while more recently a whole genome sequencing method was reported. In this study, three distinct algorithms for serotype assignment from genomic data were assessed using a panel of 790 clinical isolates.MethodsThe first approach utilised the entire capsular locus coupled with a mapping methodology. The second approach continues from the first and utilised a SNP-based methodology across the conserved cpsD-G region to differentiate serotypes Ia-VII and IX. Finally the third approach used the variable cpsG –K region coupled with a mapping methodology. All three approaches were assessed for typeability (percentage of isolates assigned a serotype) and concordance to the latex agglutination methodology.ResultsFollowing comparisons, the third approach using the variable cpsG-K region demonstrated the best performance with 99.9% typeability and 86.7% concordance. Overall, of the 105 discordant isolates, 71 were resolved following retesting of latex agglutination and whole genome sequencing, 20 failed to assign a serotype using latex agglutination and only 14 were found to be truly discordant on re-testing. Comparison of this final approach with the previously described assembly-based approach returned identical results.ConclusionsThese results demonstrated that molecular capsular typing using whole genome sequencing and a mapping-based approach is a viable alternative to the traditional, latex agglutination-based serotyping method and can be implemented in a public health microbiology setting.

Social network ad allocation and optimization: a geometric mapping-based approach

With the increasing popularity of online social networks (SNS), many advertisers choose to post their advertisements (ads) within SNS. Advertising activity on SNS has grown rapidly and is now a billion-dollar business. For example, Facebook has reached more than 1 million active advertisers who contribute 90% of its revenue. In the SNS advertising model, advertisers participating in a SNS ad campaign benefit from the effects of viral marketing and network diffusion. Modern SNS serve as advertising agents and take advantage of the network diffusion to attract advertisers and charge for the cascading impressions. The optimal ad allocation task is the problem of choosing the ad allocation plan that maximizes revenue for the SNS. Considering that users have diffusion abilities and limited daily impressions, and advertisers have various bidding prices and budget concerns, a feasible plan that obeys the constraints is difficult to find. The solution to this problem lies in the space of \({\mathbb {N}}_0^{|Ads|\times |Users|}\), which makes direct optimization unattractive. In this work, we study SNS advertising business models and formulate the SNS ad allocation problem. We show the problem is NP-hard and propose two dimension reduction schemes together with novel relaxation techniques. Our dimension reduction technique is formulated based on SNS user profile-based bidding scenario as well as social influence-based billing policies. We show the core ideas for dimension reduction are applicable to generalized assignment problems in bipartite graphs. We further draw connections between geometric mapping for complex network and the SNS ad allocation problem and map the SNS onto 2-D geometric space in order to relax the problem to geometric region allocation problems. We develop an optimization framework and solve the relaxed problem as a series of linear programs. Our proposed method is able to reduce the dimensionality of the original problem significantly, run two to four orders of magnitude faster, and reach 95% of the optimal solution. In addition, we discuss several extensions of our approach, including shape design in the geometric space for incorporating domain constraints in allocation strategies, more comprehensive real-world social influence models, as well as an alternative relaxation approach and its application in generalized assignment problems.

Polynomial and multivariate mapping-based triple-key approach for secure key distribution in wireless sensor networks

Distribution of cryptographic key plays a vital role to improve the security and integrity in Wireless Sensor Networks (WSN). Due to high resilience to the attacks, the pairwise key distribution schemes are highly preferred than other schemes. However, due to the resource constraints on the nodes and the threat of node capture, pairwise key establishment in the sensor networks is a challenging task. The security performance of these schemes decreases with the increase in the number of compromised nodes. To overcome these issues, a Polynomial and Multivariate Mapping-Based Triple-Key (PMMTK) distribution approach is proposed in this paper. It involves calculation of individual key of the nodes and common triple-key. Our proposed method achieves higher data freshness, throughput, average number of clusters, overall network lifetime, network connectivity, better packet delivery ratio and lower end-to-end delay, average number of key establishment and total authentication overhead.

Machine Tool Energy Efficiency – A Component Mapping-Based Approach

The European Commission outlined the energy-related products (ErPs) meant to be labelled and regulated in order to achieve the goals to reduce the European amount of CO2-emissions by 20% by 2020 compared to projections. Machine tools (MTs) fulfill all mandatory criteria to be categorized as ErP, namely: significant sales volume, significant environmental impact and significant improvement potential. However, the energy consumption and energy efficiency of MTs strongly depend on their utilization. A generic evaluation approach for quantifying a MT’s energy efficiency is still under development by the working group ISO/TC 39/WG 12, which drives forward the ISO 14955 series for environmental evaluation of MTs.This work presents an approach for a generic energy efficiency evaluation of MTs. Component-specific behavior is investigated and aggregated in order to entirely describe the power consumption of a MT for any utilization by power mapping. Power maps contain all possible operational scenarios under the condition of the component boundaries. The approach allows a generic MT evaluation independent on the utilization and forms the base for future MT energy efficiency labelling. The presented approach is applied and validated in a practical case study.

WGS for surveillance of antimicrobial resistance: a pilot study to detect the prevalence and mechanism of resistance to azithromycin in a UK population of non-typhoidal Salmonella.

WGS and phenotypic methods were used to determine the prevalence of azithromycin resistance in Salmonella enterica isolates from the UK and to identify the underlying mechanisms of resistance. WGS by Illumina HiSeq was carried out on 683 Salmonella spp. isolates. Known genes associated with azithromycin resistance were detected by WGS using a mapping-based approach. Macrolide resistance determinants were identified and the genomic context of these elements was assessed by various bioinformatics tools. Susceptibility testing was in accordance with EUCAST methodology (MIC ≤16 mg/L). Fifteen isolates of non-typhoidal Salmonella enterica belonging to serovars Salmonella Blockley, Salmonella Typhimurium, Salmonella Thompson, Salmonella Ridge and Salmonella Kentucky showed resistance or decreased susceptibility to azithromycin (from 6 to >16 mg/L) due to the presence of macrolide resistance genes mphA, mphB or mefB. These genes were either plasmid or chromosomally mediated. Azithromycin-resistant Salmonella Blockley isolates harboured a macrolide inactivation gene cluster, mphA-mrx-mphr(A), within a novel Salmonella azithromycin resistance genomic island (SARGI) determined by MinION sequencing. This is the first known chromosomally mediated mphA gene cluster described in salmonellae. Phylogenetic analysis and epidemiological information showed that mphA Salmonella Blockley isolates were not derived from a single epidemiologically related event. The azithromycin MICs of the 15 Salmonella spp. isolates showed that the presence of the mphA gene was associated with MIC ≥16 mg/L, while the presence of mefB or mphB was not. Azithromycin resistance due to acquisition of known macrolide resistance genes was seen in four different Salmonella serovars and can be either plasmid-encoded or chromosomally encoded.

MOST: a modified MLST typing tool based on short read sequencing.

Multilocus sequence typing (MLST) is an effective method to describe bacterial populations. Conventionally, MLST involves Polymerase Chain Reaction (PCR) amplification of housekeeping genes followed by Sanger DNA sequencing. Public Health England (PHE) is in the process of replacing the conventional MLST methodology with a method based on short read sequence data derived from Whole Genome Sequencing (WGS). This paper reports the comparison of the reliability of MLST results derived from WGS data, comparing mapping and assembly-based approaches to conventional methods using 323 bacterial genomes of diverse species. The sensitivity of the two WGS based methods were further investigated with 26 mixed and 29 low coverage genomic data sets from Salmonella enteridis and Streptococcus pneumoniae. Of the 323 samples, 92.9% (n = 300), 97.5% (n = 315) and 99.7% (n = 322) full MLST profiles were derived by the conventional method, assembly- and mapping-based approaches, respectively. The concordance between samples that were typed by conventional (92.9%) and both WGS methods was 100%. From the 55 mixed and low coverage genomes, 89.1% (n = 49) and 67.3% (n = 37) full MLST profiles were derived from the mapping and assembly based approaches, respectively. In conclusion, deriving MLST from WGS data is more sensitive than the conventional method. When comparing WGS based methods, the mapping based approach was the most sensitive. In addition, the mapping based approach described here derives quality metrics, which are difficult to determine quantitatively using conventional and WGS-assembly based approaches.

BIG-MIX: A Mapping-Based Approach Towards Measuring Anonymity

As the Internet is becoming a virtual platform for people’s sharing information and communicating daily, more and more people are connected together and the contact area between their personal privacy and the outside world turn to be unprecedented large. Therefore, the users of the Internet are faced with an enormous privacy threat. Anonymous P2P technology is a good way to solve the above problems. The ultimate goal of all anonymous P2P systems is to obtain the anonymity. Anonymity assessment on different anonymous P2P systems can be used to compare the amount of anonymity offered by different systems or to adjust the parameter setting of the same system according to different need, which is of great guiding significance to improve anonymous communication systems. In this paper, from the angle of relationship anonymity between the sender and the receiver, we presents the BIG-MIX anonymity method, taking the paths cross of different messages into consideration, to make anonymity assessment on anonymous P2P networks with the use of mapping. And with this method, this paper makes a quantitative analysis on the relationship anonymity of anonymous P2P networks under the mechanism of Threshold Mixes, Timed Mixes or Pool Mixes.

Test case optimisation a nature inspired approach using bacteriologic algorithm

Software testing is prime concern for the software industry and researchers. In the software testing process test cases play an important and significant role. Optimisations of test cases are essential to test the software effectively. Finding maximum number of faults and rectifying them before actual software release is most complex and critical during software development process. This paper deals with software test case optimisation using bacteriologic algorithm BA and requirement mapping-based approach. Test case optimisation deals with selecting effective test cases having maximum code coverage and fault detection capability, consequently minimising and prioritising the test cases.

Variation in DNAH1 may contribute to primary ciliary dyskinesia

BackgroundPrimary Ciliary Dyskinesia (PCD) is a genetically heterogeneous ciliopathy caused by ultrastructural defects in ciliary or flagellar structure and is characterized by a number of clinical symptoms including recurrent respiratory infections progressing to permanent lung damage and infertility.Case presentationHere we describe our search to delineate the molecular basis in two affected sisters with clinically diagnosed PCD from a consanguineous Saudi Arabian family, in which all known genes have been excluded. A homozygosity mapping-based approach was utilized that ultimately identified one single affected-shared region of homozygosity using 10 additional unaffected family members. A plausible candidate gene was directly sequenced and analyzed for mutations. A novel homozygous missense aberration (p.Lys1154Gln) was identified in both sisters in the DNAH1 gene that segregated completely with the disease phenotype. Further confirmation of this interesting variant was provided by exome-wide analysis in the proband.ConclusionMolecular variation in DNAH1 may play a role in PCD and its potential contribution should be considered in patients where all known genes are excluded.Electronic supplementary materialThe online version of this article (doi:10.1186/s12881-015-0162-5) contains supplementary material, which is available to authorized users.

Quantification of Biomass Production Potentials from Trees Outside Forests—A Case Study from Central Germany

Woody biomass of trees outside forests (TOF) is gaining increasing interest in many countries as it is a renewable energy source that has not been managed for bioenergy production. Our case study describes two independent approaches to assess regional area of TOF as a means for the biomass production potential of TOF within a study region in Germany, the Gottingen district (area: 1,118 km2): (1) a statistical sampling with field inventory data, and (2) an area-wide GIS-mapping approach based on open-access aerial imagery. For our particular study, the differences between the mapping-based approach and the sample-based approach were minor (sampling: 24.37 ha and 16,670 t of dry wood per year with a relative standard error 11.6 % vs. area-wide mapping: 24.35 ha and 16,055 t; standard error not available). Due to a minor difference of only 3.7 % between the two approaches, we conclude that area-wide mapping serves as a sound basis for a quantification of bioenergy potentials from TOF. It also shown that only about 62 % of all TOF objects (74 % of the total annual biomass production) would be directly accessible via the existing road infrastructure (without heavy machinery). In terms of available end-use energy, the regional biomass potential translates to an annual amount of 233 TJ which, in turn, reflects only about 0.9 % of the annual end-use energy demand in the study area. This marginal contribution to the region’s energy supply is due to the fact that TOF covers only around 24 km2 (~2 %) in our study area.

Customer's Flow Analysis in Physical Retail Store

Customer's Flow Analysis helps retailers to understand the actual behaviour and path of customers in their store, such as traffic, visited areas, customers’ paths, dwell time or loyalty. Online stores seem to “know” their customers better - correctly identifying them by name, remembering their last few purchases, or dynamically customizing the storefront to showcase relevant products. Retailers are now enabling their physical storefronts to provide a similar level of personalization. Although store cameras have historically been used as a form of surveillance to detect and deter shoplifting, stores are now tracking shoppers as they browse through a store to gather information about their target market, specifically what products consumers like and don’t like. In this paper, aim to developing physical retail store detail behavior and trajectory analysis. This paper proposed an in-store customers’ trajectory color-map system with Camera. It can also be applied to multi-flow to correspond with the demand for the practical application. First, we capture the images remotely. For obtaining the moving objects, this system uses Codebook Algorithm which applied to subtract the background dynamically. And do the morphological processing like erosion and dilation to the object and find the region of interest (ROI) often performed on using a mapping-based detection approach. Then apply Histogram of Oriented Gradients (HOG) on ROIs to sift the target out. After these previous steps, this system gathers statistics of the data and resample them. Therefore, we can obtain the color-map of customers’ trajectory according to the results. After all, our system provides a high quality in the results. And we are using this system now on those stores, like baby products store, woman's shoes store, and furniture store, etc.