Hybrid approaches to estimating freeway travel times using point traffic detector data

The accurate and reliable estimation of travel time based on point detector data is needed to support Intelligent Transportation System (ITS) applications. It has been found that the quality of travel time estimation is a function of the method used in the estimation and varies for different traffic conditions. In this study, two hybrid on-line travel time estimation models, and their corresponding off-line methods, were developed to achieve better estimation performance under various traffic conditions, including recurrent congestion and incidents. The first model combines the Mid-Point method, which is a speed-based method, with a traffic flow-based method. The second model integrates two speed-based methods: the Mid-Point method and the Minimum Speed method. In both models, the switch between travel time estimation methods is based on the congestion level and queue status automatically identified by clustering analysis. During incident conditions with rapidly changing queue lengths, shock wave analysis-based refinements are applied for on-line estimation to capture the fast queue propagation and recovery. Travel time estimates obtained from existing speed-based methods, traffic flow-based methods, and the models developed were tested using both simulation and real-world data. The results indicate that all tested methods performed at an acceptable level during periods of low congestion. However, their performances vary with an increase in congestion. Comparisons with other estimation methods also show that the developed hybrid models perform well in all cases. Further comparisons between the on-line and off-line travel time estimation methods reveal that off-line methods perform significantly better only during fast-changing congested conditions, such as during incidents. The impacts of major influential factors on the performance of travel time estimation, including data preprocessing procedures, detector errors, detector spacing, frequency of travel time updates to traveler information devices, travel time link length, and posted travel time range, were investigated in this study. The results show that these factors have more significant impacts on the estimation accuracy and reliability under congested conditions than during uncongested conditions. For the incident conditions, the estimation quality improves with the use of a short rolling period for data smoothing, more accurate detector data, and frequent travel time updates.

Sources and mixing state of size-resolved elemental carbon particles in a European megacity: Paris

An Aerosol Time-Of-Flight Mass Spectrometer (ATOFMS) was deployed to investigate the size-resolved chemical composition of single particles at an urban background site in Paris, France, as part of the MEGAPOLI winter campaign in January/February 2010. ATOFMS particle counts were scaled to match coincident Twin Differential Mobility Particle Sizer (TDMPS) data in order to generate hourly size-resolved mass concentrations for the single particle classes observed. The total scaled ATOFMS particle mass concentration in the size range 150–1067 nm was found to agree very well with the sum of concurrent High-Resolution Time-of-Flight Aerosol Mass Spectrometer (HR-ToF-AMS) and Multi-Angle Absorption Photometer (MAAP) mass concentration measurements of organic carbon (OC), inorganic ions and black carbon (BC) (R2 = 0.91). Clustering analysis of the ATOFMS single particle mass spectra allowed the separation of elemental carbon (EC) particles into four classes: (i) EC attributed to biomass burning (ECbiomass), (ii) EC attributed to traffic (ECtraffic), (iii) EC internally mixed with OC and ammonium sulfate (ECOCSOx), and (iv) EC internally mixed with OC and ammonium nitrate (ECOCNOx). Average hourly mass concentrations for EC-containing particles detected by the ATOFMS were found to agree reasonably well with semi-continuous quantitative thermal/optical EC and optical BC measurements (r2 = 0.61 and 0.65–0.68 respectively, n = 552). The EC particle mass assigned to fossil fuel and biomass burning sources also agreed reasonably well with BC mass fractions assigned to the same sources using seven-wavelength aethalometer data (r2 = 0.60 and 0.48, respectively, n = 568). Agreement between the ATOFMS and other instrumentation improved noticeably when a period influenced by significantly aged, internally mixed EC particles was removed from the intercomparison. 88% and 12% of EC particle mass was apportioned to fossil fuel and biomass burning respectively using the ATOFMS data compared with 85% and 15% respectively for BC estimated from the aethalometer model. On average, the mass size distribution for EC particles is bimodal; the smaller mode is attributed to locally emitted, mostly externally mixed EC particles, while the larger mode is dominated by aged, internally mixed ECOCNOx particles associated with continental transport events. Periods of continental influence were identified using the Lagrangian Particle Dispersion Model (LPDM) "FLEXPART". A consistent minimum between the two EC mass size modes was observed at approximately 400 nm for the measurement period. EC particles below this size are attributed to local emissions using chemical mixing state information and contribute 79% of the scaled ATOFMS EC particle mass, while particles above this size are attributed to continental transport events and contribute 21% of the EC particle mass. These results clearly demonstrate the potential benefit of monitoring size-resolved mass concentrations for the separation of local and continental EC emissions. Knowledge of the relative input of these emissions is essential for assessing the effectiveness of local abatement strategies.

Single particle characterization using the soot particle aerosol mass spectrometer (SP-AMS)

Understanding the impact of atmospheric black carbon (BC) containing particles on human health and radiative forcing requires knowledge of the mixing state of BC, including the characteristics of the materials with which it is internally mixed. In this study, we demonstrate for the first time the capabilities of the Aerodyne Soot-Particle Aerosol Mass Spectrometer equipped with a light scattering module (LS-SP-AMS) to examine the mixing state of refractory BC (rBC) and other aerosol components in an urban environment (downtown Toronto). K-means clustering analysis was used to classify single particle mass spectra into chemically distinct groups. One resultant cluster is dominated by rBC mass spectral signals (C+1 to C+5) while the organic signals fall into a few major clusters, identified as hydrocarbon-like organic aerosol (HOA), oxygenated organic aerosol (OOA), and cooking emission organic aerosol (COA). A nearly external mixing is observed with small BC particles only thinly coated by HOA ( 28% by mass on average), while over 90% of the HOA-rich particles did not contain detectable amounts of rBC. Most of the particles classified into other inorganic and organic clusters were not significantly associated with BC. The single particle results also suggest that HOA and COA emitted from anthropogenic sources were likely major contributors to organic-rich particles with low to mid-range aerodynamic diameter (dva). The similar temporal profiles and mass spectral features of the organic clusters and the factors from a positive matrix factorization (PMF) analysis of the ensemble aerosol dataset validate the conventional interpretation of the PMF results.

Stratégies reproductives et sex-ratio chez le termite Cavitermes tuberosus (Emerson, 1925) -- Biologie des Organismes et Ecologie

Recently shown in some termites, Asexual Queen Succession (AQS) is a reproductive strategy in which the primary queen is replaced by numerous parthenogenetically-produced neotenic queens that mate with the primary king. In contrast, the workforce and alate dispersers are produced sexually. If the primary king is replaced by a sexually-produced neotenic son, the matings between neotenic male and females beget asymmetries in the reproductive value of alates, promoting a female-biased alate sex-ratio. Cavitermes tuberosus (Termitidae: Termitinae) is a soil-feeding tropical species, which shows parthenogenetically-produced neotenics and an AQS syndrome. Our work aims to characterize the reproductive strategies in this species by determining (i) the developmental scheme, (ii) the genetic origin of sexuals, (iii) the level of genetic structure (analysis of 65 nests distributed in 14 sites) and (iv) the alate sex-ratio.Our results show that (i) neotenic females develop from the third or fourth nymphal instar; (ii) the majority of neotenic females (82%) are parthenogenetically-produced while only 2% of female alates are so; (iii) nests are differentiated within sites, indicating that the foundation of new nests mainly occurs by nuptial flights; (iv) numerical sex-ratio of alate-destined sexuals is balanced (SRN=0.509, IC95%=0.497-0.522) while investment sex-ratio is slightly female-biased (SRE=0.529, IC95%=0.517-0.542). Altogether, our results demonstrate AQS and its implications in C. tuberosus, and reveal particularities compared to other species in which AQS has been demonstrated: neotenic-headed nests are less frequent than primary-headed ones and neotenic females never become physogastric. AQS is found in various ecological contexts and seems phylogenetically more widespread than previously thought. This strategy shows some evolutionary advantages but these seem to differ depending on species.

Sources of antibiotic resistance in the environment

The discovery of antibiotics was a major breakthrough in medicine. However, short after their introduction in clinical practice resistant bacteria were detected. Nowadays, antibiotic resistance constitutes a serious public health problem. In hospital settings, with high resistance levels, reducing drastically the therapeutic options. Carbapenems are last-resort antibiotics used in Portugal, only in hospitals, to treat serious infections. Bacterial resistance towards this class of antibiotics has increased during last years. In Gram-negative bacteria the production of carbapenemases is a common resistance mechanism. OXA-48 is a carbapenemase of Ambler class D and represents a major concern for human health. It is frequently detected in clinical isolates of Enterobacteriaceae. There are few studies suggesting that genes encoding for OXA-48 variants originated from genes present in the chromosome of members of genus Shewanella, and have disseminated to Enterobacteriaceae members, associated with mobile genetic elements. The aim of this study was to characterize strains from different sources of Shewanella to confirm its role as OXA-48 progenitor. For this, the phylogenetic affiliation of 33 strains of Shewanella was performed by 16SrDNA and gyrB sequencing. The most common species were S. hafniensis and S. xiamenensis, but also S. aestuarii, S. baltica, S. indica, S. haliotis, S. putrefaciens, S. algidipiscicola, S. irciniae, S. algae and S. fodinae were identified. blaOXA-48-like genes were detected in 21 isolates: S. hafniensis (8/8), S. xiamenensis (5/5), S. baltica (4/4), S. algae (1/1), S. fodinae (1/1), S. putrefaciens (1/2) and S. algidipiscicola (1/2). Sequence analysis revealed that genes encoded enzymes identical to OXA-48, OXA-181 and OXA-204 but also new variants differing from OXA-48 from 2 to 81 aminoacids. Genetic context analysis revealed the C15 gene upstream and lysR gene downstream, identical to what has been identified so far flanking blaOXA-48-like genes in Shewanella spp. The assessment of antibiotic susceptibility was performed for all isolates using the disk diffusion method. In general, it was observed a great sensitivity for all antibiotics except to amoxicillin and aztreonam. Multidrug resistance was detected in only 1 isolate. Other resistance genes and the presence of integrons were not identified. Plasmids were detected in 30.3% isolates (10/ 33). These results reinforce the role of Shewanella spp. as origin of blaOXA-48-like genes.

Human Ezrin: Prediction of the 3D structure and interactions with mTOR

The protein Ezrin, is a member of the ERM family (Ezrin, Radixin and Moesin) that links the F-actin to the plasma membrane. The protein is made of three domains namely the FERM domain, a central α-helical domain and the CERMAD domain. The residues in Ezrin such as Ser66, Tyr145, Tyr353 and Tyr477 regulate the function of the protein through phosphorylation. The protein is found in two distinct conformations of active and dormant (inactive) state. The initial step during the conformation change is the breakage of intramolecular interaction in dormant Ezrin by phosphorylation of residue Thr567. The dormant structure of human Ezrin was predicted computationally since only partial active form structure was available. The validation analysis showed that 99.7% residues were positioned in favored, allowed and generously allowed regions of the Ramachandran plot. The Z-score of Ezrin was −7.36, G-factor was 0.1, and the QMEAN score of the model was 0.61 indicating a good model for human Ezrin. The comparison of the conformations of the activated and dormant Ezrin showed a major shift in the F2 lobe (residues 142-149 and 161-177) while changes in the conformation induced mobility shifts in lobe F3 (residues 261 to 267). The 3D positions of the phosphorylation sites Tyr145, Tyr353, Tyr477, Tyr482 and Thr567 were also located. Using targeted molecular dynamic simulation, the molecular movements during conformational change from active to dormant were visualized. The dormant Ezrin auto-inhibits itself by a head-to-tail interaction of the N-terminal and C-terminal residues. The trajectory shows the breakage of the interactions and mobility of the CERMAD domain away from the FERM domain. Protein docking and clustering analysis were used to predict the residues involved in the interaction between dormant Ezrin and mTOR. Residues Tyr477 and Tyr482 were found to be involved in interaction with mTOR.

MLCut: exploring multi-level cuts in dendrograms for biological data.

Choosing a single similarity threshold for cutting dendrograms is not sufficient for performing hierarchical clustering analysis of heterogeneous data sets. In addition, alternative automated or semi-automated methods that cut dendrograms in multiple levels make assumptions about the data in hand. In an attempt to help the user to find patterns in the data and resolve ambiguities in cluster assignments, we developed MLCut: a tool that provides visual support for exploring dendrograms of heterogeneous data sets in different levels of detail. The interactive exploration of the dendrogram is coordinated with a representation of the original data, shown as parallel coordinates. The tool supports three analysis steps. Firstly, a single-height similarity threshold can be applied using a dynamic slider to identify the main clusters. Secondly, a distinctiveness threshold can be applied using a second dynamic slider to identify “weak-edges” that indicate heterogeneity within clusters. Thirdly, the user can drill-down to further explore the dendrogram structure - always in relation to the original data - and cut the branches of the tree at multiple levels. Interactive drill-down is supported using mouse events such as hovering, pointing and clicking on elements of the dendrogram. Two prototypes of this tool have been developed in collaboration with a group of biologists for analysing their own data sets. We found that enabling the users to cut the tree at multiple levels, while viewing the effect in the original data, is a promising method for clustering which could lead to scientific discoveries.

Criblage génétique à la recherche de nouveaux gènes essentiels influençant l’homéostasie des télomères chez Saccharomyces cerevisiae : Un défi de tailles.

Résumé : Chez la levure Saccharomyces cerevisiae, la régulation de la longueur des télomères témoigne de la compensation entre mécanismes d'érosion (exonucléases, réplication semi-conservative et résection), facteurs d’élongation (la télomérase, transcriptase inverse à l'action retrouvée dans 90% des cancers humains) et actions de diverses protéines de régulation télomérique spécifiques, conférant aux télomères leur caractère de « capuchon » protégeant les extrémités des chromosomes eucaryotes. Afin de savoir si les gènes impossibles à déléter, car essentiels à la survie cellulaire, jouent aussi un rôle sur l’homéostasie télomérique, j'ai réalisé un criblage génétique utilisant des mutants tet-off de la levure pour lesquels la sous-expression considérable d'un gène essentiel a été induite de façon conditionnelle. Ceci permet d’étudier les effets qui en résultent sur l’homéostasie des télomères. Au total, mon travail a traité plus de 662 gènes essentiels pour lesquels j'ai analysé le phénotype de longueur des télomères de manière qualitative par comparaison des télomères de souches mutées par rapport à ceux de souches de type sauvage. Puis, grâce à l’amélioration technique que j'ai mise au point, la quantification de la taille des répétitions télomériques de 300 de ces souches a déjà pu être précisément analysée. Il est notable que tous les gènes essentiels étudiés ici ont des effets très différents qui résultent en des chromosomes possédant des télomères de longueur très inégale. Pour près de 40% des mutants analysés, les tailles de télomères sont apparues critiquement différentes de celles normalement présentées par la levure, beaucoup de ces gènes essentiels étant impliqués dans des mécanismes affectant le cycle cellulaire, la réparation, etc. La majorité des gènes criblés apporte un important complément d’information dans une littérature presque inexistante sur les effets de gènes essentiels de la levure au niveau de la biologie des télomères. C’est le cas des mutations de YHR122W (montrant des télomères long) et YOR262W (télomères courts), deux gènes qui sont apparus d'après mes résultats nécessaires au maintien de l'homéostasie télomérique (prenant place dans un grand ensemble de gènes que j’ai dénommé gènes ETL pour Essential for Telomere Length Maintenance).

Extracting DNA from ‘jaws’: high yield and quality from archived tiger shark (Galeocerdo cuvier) skeletal material

Archived specimens are highly valuable sources of DNA for retrospective genetic/genomic analysis. However, often limited effort has been made to evaluate and optimize extraction methods, which may be crucial for downstream applications. Here, we assessed and optimized the usefulness of abundant archived skeletal material from sharks as a source of DNA for temporal genomic studies. Six different methods for DNA extraction, encompassing two different commercial kits and three different protocols, were applied to material, so-called bio-swarf, from contemporary and archived jaws and vertebrae of tiger sharks (Galeocerdo cuvier). Protocols were compared for DNA yield and quality using a qPCR approach. For jaw swarf, all methods provided relatively high DNA yield and quality, while large differences in yield between protocols were observed for vertebrae. Similar results were obtained from samples of white shark (Carcharodon carcharias). Application of the optimized methods to 38 museum and private angler trophy specimens dating back to 1912 yielded sufficient DNA for downstream genomic analysis for 68% of the samples. No clear relationships between age of samples, DNA quality and quantity were observed, likely reflecting different preparation and storage methods for the trophies. Trial sequencing of DNA capture genomic libraries using 20 000 baits revealed that a significant proportion of captured sequences were derived from tiger sharks. This study demonstrates that archived shark jaws and vertebrae are potential high-yield sources of DNA for genomic-scale analysis. It also highlights that even for similar tissue types, a careful evaluation of extraction protocols can vastly improve DNA yield.

RELATIONSHIP BETWEEN COLUMN DENSITY AND SURFACE MIXING RATIO FOR O3 AND NO2: IMPLICATIONS FOR SATELLITE OBSERVATIONS AND THE IMPACTS OF VERTICAL MIXING

Satellites have great potential for diagnosis of surface air quality conditions, though reduced sensitivity of satellite instrumentation to the lower troposphere currently impedes their applicability. One objective of the NASA DISCOVER-AQ project is to provide information relevant to improving our ability to relate satellite-observed columns to surface conditions for key trace gases and aerosols. In support of DISCOVER-AQ, this dissertation investigates the degree of correlation between O3 and NO2 column abundance and surface mixing ratio during the four DISCOVER-AQ deployments; characterize the variability of the aircraft in situ and model-simulated O3 and NO2 profiles; and use the WRF-Chem model to further investigate the role of boundary layer mixing in the column-surface connection for the Maryland 2011 deployment, and determine which of the available boundary layer schemes best captures the observations. Simple linear regression analyses suggest that O3 partial column observations from future satellite instruments with sufficient sensitivity to the lower troposphere may be most meaningful for surface air quality under the conditions associated with the Maryland 2011 campaign, which included generally deep, convective boundary layers, the least wind shear of all four deployments, and few geographical influences on local meteorology, with exception of bay breezes. Hierarchical clustering analysis of the in situ O3 and NO2 profiles indicate that the degree of vertical mixing (defined by temperature lapse rate) associated with each cluster exerted an important influence on the shapes of the median cluster profiles for O3, as well as impacted the column vs. surface correlations for many clusters for both O3 and NO2. However, comparisons to the CMAQ model suggest that, among other errors, vertical mixing is overestimated, causing too great a column-surface connection within the model. Finally, the WRF-Chem model, a meteorology model with coupled chemistry, is used to further investigate the impact of vertical mixing on the O3 and NO2 column-surface connection, for an ozone pollution event that occurred on July 26-29, 2011. Five PBL schemes were tested, with no one scheme producing a clear, consistent “best” comparison with the observations for PBLH and pollutant profiles; however, despite improvements, the ACM2 scheme continues to overestimate vertical mixing.

Assessing the mitochondrial DNA diversity of the Chagas disease vector Triatoma sordida (Hemiptera: Reduviidae)

Triatoma sordida is a species that transmits Trypanosoma cruzi to humans. In Brazil, T. sordida currently deserves special attention because of its wide distribution, tendency to invade domestic environments and vectorial competence. For the planning and execution of control protocols to be effective against Triatominae, they must consider its population structure. In this context, this study aimed to characterise the genetic variability of T. sordida populations collected in areas with persistent infestations from Minas Gerais, Brazil. Levels of genetic variation and population structure were determined in peridomestic T. sordida by sequencing a polymorphic region of the mitochondrial cytochrome b gene. Low nucleotide and haplotype diversity were observed for all 14 sampled areas; π values ranged from 0.002-0.006. Most obtained haplotypes occurred at low frequencies, and some were exclusive to only one of the studied populations. Interpopulation genetic diversity analysis revealed strong genetic structuring. Furthermore, the genetic variability of Brazilian populations is small compared to that of Argentinean and Bolivian specimens. The possible factors related to the reduced genetic variability and strong genetic structuring obtained for studied populations are discussed in this paper.

The Early Intestinal Microbiota of Healthy Korean Newborns

Background: The microflora hypothesis may be the underlying explanation for the growth of inflammatory disease. In addition to many known affecting factors, knowing the gut microbiota of healthy newborns can help to understand the gut immunity and modulate it. Objectives: This study examined the microbiota of healthy newborns from urban regions. Patients and Methods: We enrolled 128 full-term newborns, born at Seoul St. Mary and St. Paul hospital from January 2009 to February 2010. All 143 samples of feces were cultivated in six culture plates to determine the amounts of total bacteria, anaerobes, gram-positive bacteria, coliforms, lactobacilli, and bifidobacteria. The samples were evaluated with a bivariate correlation between coliforms and lactobacilli. Terminal restriction fragment length polymorphism (T-RFLP) analysis with HhaI and MspI and a clustering analysis were performed for determination of diversity. Results: Bacteria were cultured in 61.5% of feces in the following order: anaerobes, gram-positive bacteria, lactobacilli, coliform, and bifidobacteria. The growth of total bacteria and lactobacilli increased in feces defecated after 24 hours of birth (P < 0.001, P = 0.008) and anaerobes decreased (P = 0.003). A negative correlation between the growth of lactobacilli and coliforms was found (r = -463, P < 0.001). Conclusions: This study confirms that bacterial colonization of healthy newborns born in cities is non-sterile, but has early diversification and inter-individuality.

Análise de agrupamentos dos dados de DFA oriundos de perfis elétricos de indução de poços de petróleo

The main objective of this study is to apply recently developed methods of physical-statistic to time series analysis, particularly in electrical induction s profiles of oil wells data, to study the petrophysical similarity of those wells in a spatial distribution. For this, we used the DFA method in order to know if we can or not use this technique to characterize spatially the fields. After obtain the DFA values for all wells, we applied clustering analysis. To do these tests we used the non-hierarchical method called K-means. Usually based on the Euclidean distance, the K-means consists in dividing the elements of a data matrix N in k groups, so that the similarities among elements belonging to different groups are the smallest possible. In order to test if a dataset generated by the K-means method or randomly generated datasets form spatial patterns, we created the parameter Ω (index of neighborhood). High values of Ω reveals more aggregated data and low values of Ω show scattered data or data without spatial correlation. Thus we concluded that data from the DFA of 54 wells are grouped and can be used to characterize spatial fields. Applying contour level technique we confirm the results obtained by the K-means, confirming that DFA is effective to perform spatial analysis

Advanced statistical models for the segregation, identification and measurement of coexisting sound sources

Long-term monitoring of acoustical environments is gaining popularity thanks to the relevant amount of scientific and engineering insights that it provides. The increasing interest is due to the constant growth of storage capacity and computational power to process large amounts of data. In this perspective, machine learning (ML) provides a broad family of data-driven statistical techniques to deal with large databases. Nowadays, the conventional praxis of sound level meter measurements limits the global description of a sound scene to an energetic point of view. The equivalent continuous level Leq represents the main metric to define an acoustic environment, indeed. Finer analyses involve the use of statistical levels. However, acoustic percentiles are based on temporal assumptions, which are not always reliable. A statistical approach, based on the study of the occurrences of sound pressure levels, would bring a different perspective to the analysis of long-term monitoring. Depicting a sound scene through the most probable sound pressure level, rather than portions of energy, brought more specific information about the activity carried out during the measurements. The statistical mode of the occurrences can capture typical behaviors of specific kinds of sound sources. The present work aims to propose an ML-based method to identify, separate and measure coexisting sound sources in real-world scenarios. It is based on long-term monitoring and is addressed to acousticians focused on the analysis of environmental noise in manifold contexts. The presented method is based on clustering analysis. Two algorithms, Gaussian Mixture Model and K-means clustering, represent the main core of a process to investigate different active spaces monitored through sound level meters. The procedure has been applied in two different contexts: university lecture halls and offices. The proposed method shows robust and reliable results in describing the acoustic scenario and it could represent an important analytical tool for acousticians.

Clustering algorithms for anti-money laundering using graph theory and social network analysis

HEMOLIA (a project under European community’s 7th framework programme) is a new generation Anti-Money Laundering (AML) intelligent multi-agent alert and investigation system which in addition to the traditional financial data makes extensive use of modern society’s huge telecom data source, thereby opening up a new dimension of capabilities to all Money Laundering fighters (FIUs, LEAs) and Financial Institutes (Banks, Insurance Companies, etc.). This Master-Thesis project is done at AIA, one of the partners for the HEMOLIA project in Barcelona. The objective of this thesis is to find the clusters in a network drawn by using the financial data. An extensive literature survey has been carried out and several standard algorithms related to networks have been studied and implemented. The clustering problem is a NP-hard problem and several algorithms like K-Means and Hierarchical clustering are being implemented for studying several problems relating to sociology, evolution, anthropology etc. However, these algorithms have certain drawbacks which make them very difficult to implement. The thesis suggests (a) a possible improvement to the K-Means algorithm, (b) a novel approach to the clustering problem using the Genetic Algorithms and (c) a new algorithm for finding the cluster of a node using the Genetic Algorithm.