Average Spearman Rank Correlation Research Articles

Hydrocarbon retention on activated carbon: Preservation of fire debris evidence

Fire debris is collected and analyzed to determine whether volatile hydrocarbons of ignitable liquids (IL) are present. Typically, the hydrocarbons from fire debris are separated from the debris by adsorption onto activated carbon. Laboratory generated fire debris were sampled by adsorption onto activated charcoal strips (ACS) where half of the ACS was analyzed by gas chromatography – mass spectrometry (GC–MS) and the other half was preserved for 14 years [1,2]. Reference ignitable liquids are important in the detection and identification of ignitable liquid residues (ILR) from fire debris [3]. Reference IL contain 400 µl of IL adsorbed onto 0.5 g of granular activated carbon (GAC) and were preserved for 21 years. Comparisons of the chromatographic profiles at Day 0 and Year X (X = 14 or 21) were performed by Spearman rank correlation of selected peak’s intensities and visual inspection to determine how well these samples were preserved. The fire debris samples on ACS had an average Spearman rank correlation coefficient of 0.90 with a standard deviation of 0.11. Thirty-six percent of them demonstrated no change in the chromatographic profile and 64 % of them demonstrated minimal desorption in the chromatographic profile. The GAC samples had an average Spearman rank correlation coefficient of 0.95 with a standard deviation of 0.12. Eighty-two percent of them demonstrated no change in the chromatographic profile. This study reveals that hydrocarbons of ignitable liquids on activated carbon following established preservation procedures remain useful for forensic purposes after long-term storage.

Continental-scale evaluation of a fully distributed coupled land surface and groundwater model, ParFlow-CLM (v3.6.0), over Europe

Abstract. High-resolution large-scale predictions of hydrologic states and fluxes are important for many multi-scale applications, including water resource management. However, many of the existing global- to continental-scale hydrological models are applied at coarse resolution and neglect more complex processes such as lateral surface and groundwater flow, thereby not capturing smaller-scale hydrologic processes. Applications of high-resolution and physically based integrated hydrological models are often limited to watershed scales, neglecting the mesoscale climate effects on the water cycle. We implemented an integrated, physically based coupled land surface groundwater model, ParFlow-CLM version 3.6.0, over a pan-European model domain at 0.0275∘ (∼3 km) resolution. The model simulates a three-dimensional variably saturated groundwater-flow-solving Richards equation and overland flow with a two-dimensional kinematic wave approximation, which is fully integrated with land surface exchange processes. A comprehensive evaluation of multiple hydrologic variables including discharge, surface soil moisture (SM), evapotranspiration (ET), snow water equivalent (SWE), total water storage (TWS), and water table depth (WTD) resulting from a 10-year (1997–2006) model simulation was performed using in situ and remote sensing (RS) observations. Overall, the uncalibrated ParFlow-CLM model showed good agreement in simulating river discharge for 176 gauging stations across Europe (average Spearman's rank correlation (R) of 0.77). At the local scale, ParFlow-CLM model performed well for ET (R>0.94) against eddy covariance observations but showed relatively large differences for SM and WTD (median R values of 0.7 and 0.50, respectively) when compared with soil moisture networks and groundwater-monitoring-well data. However, model performance varied between hydroclimate regions, with the best agreement to RS datasets being shown in semi-arid and arid regions for most variables. Conversely, the largest differences between modeled and RS datasets (e.g., for SM, SWE, and TWS) are shown in humid and cold regions. Our findings highlight the importance of including multiple variables using both local-scale and large-scale RS datasets in model evaluations for a better understanding of physically based fully distributed hydrologic model performance and uncertainties in water and energy fluxes over continental scales and across different hydroclimate regions. The large-scale, high-resolution setup also forms a basis for future studies and provides an evaluation reference for climate change impact projections and a climatology for hydrological forecasting considering the effects of lateral surface and groundwater flows.

An investigation to offer conclusive recommendations on suitable benefit/cost criteria-based normalization methods for TOPSIS

Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is a popular multi-criteria decision-making method that ranks the available alternatives by examining the ideal-positive and ideal-negative solutions for each decision criterion. The first step of using TOPSIS is to normalize the presence of incommensurable data in the decision matrix. There are several normalization methods, and the choice of these methods does affect TOPSIS results. As such, some efforts were made in the past to compare and recommend suitable normalization methods for TOPSIS. However, such studies merely compared a limited collection of normalization methods or used a noncomprehensive procedure to evaluate each method's suitability, leading to equivocal recommendations. This study, therefore, employed an alternate, comprehensive procedure to evaluate and recommend suitable benefit/cost criteria-based normalization methods for TOPSIS (out of ten methods extracted from past literature). The procedure was devised based on three evaluation metrics: the average Spearman's rank correlation, average Pearson correlation, and standard deviation metrics, combined with the Borda count technique.•The first study examined the suitability of ten benefit/cost criteria-based normalization methods over TOPSIS.•Users should combine the sum-based method and vector method into the TOPSIS application for safer decision-making.•The maximum method (version I) or Jüttler's-Körth's method has an identical effect on TOPSIS results.

Optimal thermochemical material selection for a hybrid thermal energy storage system for low temperature applications using multi criteria optimization technique

Thermochemical energy storage is one of the viable solutions for the increasing energy demand because of its high energy density and less heat loss during the storage phase comparing to the other methods. The selection of proper thermochemical material (TCM) is then become important in this type of thermal energy storage. This paper describes the use of Multi Criteria Decision Making (MCDM) techniques to select the optimum thermochemical material from the alternatives to be used for low temperature (<150 °C) energy storage. The best thermochemical material is selected using different MCDM techniques like simple additive method, weighted product model, Technique for Order Preference by Similarity to Ideal Solution, Evaluation based on Distance from Average Solution, Multi-Objective Optimization on The Basis of Ratio Analysis, Preference Ranking Organization Method for Enrichment Evaluation, and VIKOR. The criteria weights for optimization are evaluated by different methods such as mean weight method, standard deviation method, analytic hierarchy process, Entropy method, criteria importance through inter-criteria correlation and Compromised weight method. In all MCDM-Weighting method combinations MgCl2·6H2O ranked first (optimum material) and Na2S·9H2O ranked last. The ranking obtained from various MCDM-weighting method combination are compared with each other using average Spearman rank correlation coefficient and found that Evaluation based on Distance from Average Solution (EDAS), Multi-Objective Optimization on The Basis of Ratio Analysis (MOORA), Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE II) and VIKOR with AHP weighting method have the highest correlation coefficient (rs = 0.9857) and CRITIC-VIKOR method (rs = 0.5143) has the lowest correlation coefficient.

An interval valued neutrosophic decision-making structure for sustainable supplier selection

Evolution of supply chain management (SCM) in recent years has transformed it beyond the simple logic of benefit and economic point of views. One of such key strategic elements in establishing a sustainable and socially responsive SCM is supplier selection and performance assessment. This study brings forward a sustainable supplier evaluation structure under multiple criteria and interval valued fuzzy neutrosophic (IVFN) model. The proposed structure uses CRiteria Importance Through Inter -criteriaCorrelation (CRITIC) and combined compromised solution (CoCoSo) under IVFN environment for evaluation and selection of suppliers for a dairy company in Iran. Alternative supplier 5 (S5) emerges as the best supplier with the highest overall score (1.168). Average Spearman rank correlation coefficient between the proposed model and other well established decision-making models methods is found as 0.9651 which establishes reliability of model outcomes. The estimated zero value of Gini Index indicates that supplier 5 has a constant ranking in all considered methods. This is a practical sustainable supplier selection platform which allows decision makers in procurement and SC industries to select the most suitable supplier in any pre-determined period.

Automatic Leaderboard: Evaluation of Singing Quality Without a Standard Reference

Automatic evaluation of singing quality can be done with the help of a reference singing or the digital sheet music of the song. However, such a standard reference is not always available. In this article, we propose a framework to rank a large pool of singers according to their singing quality without any standard reference. We define musically motivated absolute measures based on pitch histogram, and relative measures based on inter-singer statistics to evaluate the quality of singing attributes such as intonation, and rhythm. The absolute measures evaluate the goodness of pitch histogram specific to a singer, while the relative measures use the similarity between singers in terms of pitch, rhythm, and timbre as an indicator of singing quality. With the relative measures, we formulate the concept of veracity or truth-finding for the ranking of singing quality. We successfully validate a self-organizing approach to rank-ordering a large pool of singers. The fusion of absolute and relative measures results in an average Spearman's rank correlation of 0.71 with human judgments in a 10-fold cross-validation experiment, which is close to the inter-judge correlation.

Cross-validation and robustness of daylight glare metrics

This study evaluates the performance and robustness of 22 established and newly proposed glare prediction metrics. Experimental datasets of daylight-dominated workplaces in office-like test rooms were collected from studies by seven research groups in six different locations (Argentina, Denmark, Germany, Israel, Japan and the United States). The variability in experimental setups, locations and research teams allowed reliable evaluation of the performance and robustness of glare metrics for daylight-dominated workplaces. Independent statistical methods were applied to individual datasets and also to one combined dataset to evaluate the performance and robustness of the 22 glare metrics. As performance and robustness are not established in literature, we defined performance as: (1) the ability of the metric value to describe the glare scale (evaluated by Spearman rank correlation), and (2) the ability of the metric to distinguish between disturbing and non-disturbing situations (evaluated by diagnostic receiver operating characteristic curve analysis tests). Furthermore, we defined robustness as the ability of a metric to deliver meaningful results when applied to different datasets and to fail as few as possible statistical tests. Average Spearman rank correlation coefficients in the range of 0.55–0.60 as well as average prediction rates to distinguish between disturbing and non-disturbing glare of 70–75% for several of the metrics indicate their reliability. The results also show that metrics considering the saturation effect as a main input in their equation perform better and are more robust in daylight-dominated workplaces than purely contrast-based metrics or purely empirical metrics. In this study, the daylight glare probability (DGP) delivered the highest performance amongst the tested metrics and was also found to be the most robust. Future research should aim to optimise the terms of glare equations which combine contrast and saturation effects, such as DGP, PGSV or UGRexp, to achieve metrics that also perform reliably in dimmer lighting conditions than the ones explored in this study.

Accurate multi-criteria decision making methodology for recommending machine learning algorithm

ObjectiveManual evaluation of machine learning algorithms and selection of a suitable classifier from the list of available candidate classifiers, is highly time consuming and challenging task. If the selection is not carefully and accurately done, the resulting classification model will not be able to produce the expected performance results. In this study, we present an accurate multi-criteria decision making methodology (AMD) which empirically evaluates and ranks classifiers’ and allow end users or experts to choose the top ranked classifier for their applications to learn and build classification models for them. Methods and materialExisting classifiers performance analysis and recommendation methodologies lack (a) appropriate method for suitable evaluation criteria selection, (b) relative consistent weighting mechanism, (c) fitness assessment of the classifiers’ performances, and (d) satisfaction of various constraints during the analysis process. To assist machine learning practitioners in the selection of suitable classifier(s), AMD methodology is proposed that presents an expert group-based criteria selection method, relative consistent weighting scheme, a new ranking method, called optimum performance ranking criteria, based on multiple evaluation metrics, statistical significance and fitness assessment functions, and implicit and explicit constraints satisfaction at the time of analysis. For ranking the classifiers performance, the proposed ranking method integrates Wgt.Avg.F-score, CPUTimeTesting, CPUTimeTraining, and Consistency measures using the technique for order performance by similarity to ideal solution (TOPSIS). The final relative closeness score produced by TOPSIS, is ranked and the practitioners select the best performance (top-ranked) classifier for their problems in-hand. FindingsBased on the extensive experiments performed on 15 publically available UCI and OpenML datasets using 35 classification algorithms from heterogeneous families of classifiers, an average Spearman's rank correlation coefficient of 0.98 is observed. Similarly, the AMD method has showed improved performance of 0.98 average Spearman's rank correlation coefficient as compared to 0.83 and 0.045 correlation coefficient of the state-of-the-art ranking methods, performance of algorithms (PAlg) and adjusted ratio of ratio (ARR). Conclusion and implicationThe evaluation, empirical analysis of results and comparison with state-of-the-art methods demonstrate the feasibility of AMD methodology, especially the selection and weighting of right evaluation criteria, accurate ranking and selection of optimum performance classifier(s) for the user's application's data in hand. AMD reduces expert's time and efforts and improves system performance by designing suitable classifier recommended by AMD methodology.

A high-density SNP Map of sunflower derived from RAD-sequencing facilitating fine-mapping of the rust resistance gene R12.

A high-resolution genetic map of sunflower was constructed by integrating SNP data from three F2 mapping populations (HA 89/RHA 464, B-line/RHA 464, and CR 29/RHA 468). The consensus map spanned a total length of 1443.84 cM, and consisted of 5,019 SNP markers derived from RAD tag sequencing and 118 publicly available SSR markers distributed in 17 linkage groups, corresponding to the haploid chromosome number of sunflower. The maximum interval between markers in the consensus map is 12.37 cM and the average distance is 0.28 cM between adjacent markers. Despite a few short-distance inversions in marker order, the consensus map showed high levels of collinearity among individual maps with an average Spearman's rank correlation coefficient of 0.972 across the genome. The order of the SSR markers on the consensus map was also in agreement with the order of the individual map and with previously published sunflower maps. Three individual and one consensus maps revealed the uneven distribution of markers across the genome. Additionally, we performed fine mapping and marker validation of the rust resistance gene R12, providing closely linked SNP markers for marker-assisted selection of this gene in sunflower breeding programs. This high resolution consensus map will serve as a valuable tool to the sunflower community for studying marker-trait association of important agronomic traits, marker assisted breeding, map-based gene cloning, and comparative mapping.

A consensus framework map of durum wheat (Triticum durum Desf.) suitable for linkage disequilibrium analysis and genome-wide association mapping

BackgroundDurum wheat (Triticum durum Desf.) is a tetraploid cereal grown in the medium to low-precipitation areas of the Mediterranean Basin, North America and South-West Asia. Genomics applications in durum wheat have the potential to boost exploitation of genetic resources and to advance understanding of the genetics of important complex traits (e.g. resilience to environmental and biotic stresses). A dense and accurate consensus map specific for T. durum will greatly facilitate genetic mapping, functional genomics and marker-assisted improvement.ResultsHigh quality genotypic data from six core recombinant inbred line populations were used to obtain a consensus framework map of 598 simple sequence repeats (SSR) and Diversity Array Technology® (DArT) anchor markers (common across populations). Interpolation of unique markers from 14 maps allowed us to position a total of 2,575 markers in a consensus map of 2,463 cM. The T. durum A and B genomes were covered in their near totality based on the reference SSR hexaploid wheat map. The consensus locus order compared to those of the single component maps showed good correspondence, (average Spearman’s rank correlation rho ρ value of 0.96). Differences in marker order and local recombination rate were observed between the durum and hexaploid wheat consensus maps. The consensus map was used to carry out a whole-genome search for genetic differentiation signatures and association to heading date in a panel of 183 accessions adapted to the Mediterranean areas. Linkage disequilibrium was found to decay below the r2 threshold = 0.3 within 2.20 cM, on average. Strong molecular differentiations among sub-populations were mapped to 87 chromosome regions. A genome-wide association scan for heading date from 27 field trials in the Mediterranean Basin and in Mexico yielded 50 chromosome regions with evidences of association in multiple environments.ConclusionsThe consensus map presented here was used as a reference for genetic diversity and mapping analyses in T. durum, providing nearly complete genome coverage and even marker density. Markers previously mapped in hexaploid wheat constitute a strong link between the two species. The consensus map provides the basis for high-density single nucleotide polymorphic (SNP) marker implementation in durum wheat.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-873) contains supplementary material, which is available to authorized users.

A comparative study of optimistic and pessimistic multicriteria decision analysis based on atanassov fuzzy sets

Following the unipolar bivariate model, this paper presents a useful method of relating optimism and pessimism to multiple criteria decision analysis within the context of Atanassov fuzzy sets. We utilize four point operators to readily estimate the adaptational outcome expectancies generated by optimistic and pessimistic attitudes. We then determine the net predisposition to represent the aggregated effects of positive and negative evaluations. On the basis of the net predispositions, we calculate the suitability function to evaluate each alternative. Accordingly, we propose the optimization model with suitability functions to deal with ill-known grades of importance. We develop algorithms to aid multicriteria decision-making processes under neutral, completely optimistic, moderately optimistic, completely pessimistic, and moderately pessimistic conditions. Furthermore, we present experimental analysis on randomly generated decision problems to investigate the ranking system's consistency and the ranking irregularity issues found in the proposed methods. We establish several valuable test criteria to discuss some types of ranking consistency and irregularities (e.g., ranking consistency, ranking correlations, ranking contradictions, and ranking inversions) that occur when the different methods are used. We designed three computational experiments to compare the ranking orders yielded by different methods and examined several comparison indices, including the consistency rate of the total orders, the contradiction rate of the best choice, and the inversion rate between the better choices and the worse ones. Finally, we provided a second-order regression model to highlight the effects of variant parameter settings on the average Spearman rank correlation coefficients.

Temporal coherence of zooplankton abundance in a tropical reservoir

Population densities in different sites are frequently observed to fluctuate synchronously, a phenomenon termed temporal coherence. The two main causes of temporal coherence are dispersal and the effects of a regional factor (Moran effect) that influence each of the populations similarly. If synchronous patterns are observed, it is possible to infer that there is a regional process (e.g., climate) exerting a uniform influence over the entire region. Here, we evaluate patterns of temporal coherence in total densities of zooplankton groups. Data were gathered at 11 sites in the CorumbaReservoir (Central Brazil) between 1996 and 2000 (n = 27 months). These sites were distributed in the main channel, arms, and tributaries of the reservoir. Reservoir-wide correlations (as estimated by the average Spearman rank correlation between temporal trajectories of abundances) were low (-0.01, 0.06, 0.23, and 0.14 for cladocerans, copepods, rotifers, and testate amoebae, respectively). In general, high temporal coherence was detected only between geographically adjacent sites and/or between sites with similar limnological characteristics. Contrary to many recent studies, these results illustrate that, in a small geographic area (i.e., a single reservoir of approximately 65 km 2 ), local processes may override the effects of regional processes or dispersal. More- over, they demonstrate that the lack of regional trajectories (i.e., time series of population densities with asynchronous patterns of fluctuation) should be considered in interpreting results obtained in long- term studies or monitoring programs based on a single site per ecosystem.

Four-Body Scoring Function for Mutagenesis

There is a need for an efficient and accurate computational method to identify the effects of single- and multiple-residue mutations on the stability and reactivity of proteins. Such a method should ideally be consistent and yet applicable in a widespread manner, i.e. it should be applied to various proteins under the same parameter settings, and have good predictive power for all of them. We develop a Delaunay tessellation-based four-body scoring function to predict the effects of single- and multiple-residue mutations on the stability and reactivity of proteins. We test our scoring function on sets of single-point mutations used by several previous studies. We also assemble a new, diverse set of 237 single- and multiple-residue mutations, from over 24 different publications. The four-body scoring function correctly predicted the changes to the stability of 169 out of 210 mutants (80.5%), and the changes to the reactivity of 17 out of 27 mutants (63%). For the mutants that had the changes in stability/reactivity quantified (using reaction rates, temperatures, etc.), an average Spearman rank correlation coefficient of 0.67 was achieved with the four-body scores. We also develop an efficient method for screening huge numbers of mutants of a protein, called combinatorial mutagenesis. In one study, 64 million mutants of a cold-shock nucleus binding domain protein 1CSQ, with six of its residues being changed to all possible (20) amino acids, were screened within a few hours on a PC, and all five stabilizing mutants reported were correctly identified as stabilizing by combinatorial mutagenesis.

The cardiac analgesic assessment scale (CAAS): a pain assessment tool for intubated and ventilated children after cardiac surgery

This study evaluated the reliability and validity of the Cardiac Analgesic Assessment Scale (CAAS) as a postoperative pain instrument for children after cardiac surgery. Two prospective studies included 69 children (aged 0-16 years) admitted to the intensive care following cardiac surgery with a sternotomy incision. Four concurrent observers performed paired observations with the CAAS or a visual analogue scale (VAS) for 32 patients. After a stimulus to the patient, two nursing observers independently scored the patient with the CAAS, and another two independent nursing observers simultaneously scored the patient using a VAS. In the second part of this study the CAAS was evaluated with respect to its ability to detect changes in pain status and responses to analgesia over time in 37 patients. Interrater reliability, represented by Lin's concordance correlation coefficient proved to be almost perfect for the CAAS score 0.97 (95% CI: 0.95, 0.99). About 91% of patients received the same total CAAS score from the two raters. The dichotomized CAAS scores of the two nurses indicated that in 97% of cases the nurses agreed upon whether there was an indication for treatment of pain. The CAAS was shown to significantly reflect changes in pain status over time. The average Spearman's rank correlation between VAS and CAAS was low (0.27), indicating that CAAS did not correlate well with VAS. This study provides evidence that postoperative pain in sedated and intubated children after cardiac surgery can be assessed reliably using a formal pain tool.

A Method for Computing the Average Spearman Rank Correlation Coefficient from Ordinally Structured Confusion Matrices

Researchers have used either average correlation or the percentage of objects correctly predicted in assessing the accuracy of attitude models. Because the two measures are not directly comparable, the ability to contrast results from different studies is obviated. A method is presented for computing the average Spearman correlation coefficient directly from confusion matrices.

A Method for Computing the Average Spearman Rank Correlation Coefficient from Ordinally Structured Confusion Matrices

A Method for Computing the Average Spearman Rank Correlation Coefficient from Ordinally Structured Confusion Matrices

The average Spearman rank correlation when ties are present: a correction.

The average Spearman rank correlation when ties are present: a correction.

Some Contributions to Average Rank Correlation Methods and to the Distribution of the Average Rank Correlation Coefficient

Abstract This report concerns the method and distribution of “average rho” or , the algebraic mean of M independent Spearman rank correlation coefficients based on equal N's of objects ranked on each of two variables. Included is a reference table of those values and probabilities of most closely associated with four conventional levels of significance, .01, .02, .05, and .10, two-tailed. Most of this table is derived from the construction of 58 exact frequency distributions of for M's > 1. Also presented are (a) general formulas for the computation of , with comparisons between the method and distribution of and Lyerly's “average Spearman rank correlation coefficient,” (b) formulas for the computation of corrected for continuity, , and of the standard score z of (c) a comparison of the results of exact computation and normal approximation which illustrates the tendency of the distribution of to approach normality as M, N, and both M and N increase, but that caution should be used when M or N is small.

Some Contributions to Average Rank Correlation Methods and to the Distribution of the Average Rank Correlation Coefficient

Abstract This report concerns the method and distribution of “average rho” or , the algebraic mean of M independent Spearman rank correlation coefficients based on equal N's of objects ranked on each of two variables. Included is a reference table of those values and probabilities of most closely associated with four conventional levels of significance, .01, .02, .05, and .10, two-tailed. Most of this table is derived from the construction of 58 exact frequency distributions of for M's > 1. Also presented are (a) general formulas for the computation of , with comparisons between the method and distribution of and Lyerly's “average Spearman rank correlation coefficient,” (b) formulas for the computation of corrected for continuity, , and of the standard score z of (c) a comparison of the results of exact computation and normal approximation which illustrates the tendency of the distribution of to approach normality as M, N, and both M and N increase, but that caution should be used when M or N is small.

The average spearman rank criterion correlation when ties are present

This note presents the average Spearman rank correlation betweenm independent rankings and an untied criterion ranking, corrected for ties in any or all of the independent rankings.