Effect of fragrance use on discrimination of individual body odor

Previous research suggests that artificial fragrances may be chosen to complement or enhance an individual's body odor, rather than simply masking it, and that this may create an odor blend with an emergent quality that is perceptually distinguishable from body odor or fragrance alone. From this, it can be predicted that a new emergent odor might be more easily identified than an individual's body odor in isolation. We used a triangle test paradigm to assess whether fragrance affects people's ability to distinguish between individual odors. Six male and six female donors provided axillary odor samples in three conditions (without fragrance, wearing their own fragrance, and wearing an assigned fragrance). In total, 296 female and 131 male participants selected the odd one from three odor samples (two from one donor, one from another; both of the same sex). We found that participants could discriminate between the odors at above chance levels in all three odor conditions. Olfactory identification ability (measured using Sniffin' Sticks) positively predicted discrimination performance, and sex differences in performance were also observed, with female raters being correct more often than men. Success rates were also higher for odors of male donors. Additionally, while performance was above chance in all conditions, individual odor discrimination varied across the three conditions. Discrimination rate was significantly higher in the "no fragrance" condition than either of the fragranced conditions. Importantly, however, discrimination rate was also significantly higher in the "own fragrance" condition than the "assigned fragrance" condition, suggesting that naturally occurring variance in body odor is more preserved when blended with fragrances that people choose for themselves, compared with other fragrances. Our data are consistent with the idea that fragrance choices are influenced by fragrance interactions with an individual's own body odor

The Machines Aren’t Taking Over (Yet): An Empirical Comparison of Traditional, Profiling, and Machine Learning Approaches to Criterion-Related Validation

Criterion-related validation (CRV) studies are used to demonstrate the effectiveness of selection procedures. However, traditional CRV studies require significant investment of time and resources, as well as large sample sizes, which often create practical challenges. New techniques, which use machine learning to develop classification models from limited amounts of data, have emerged as a more efficient alternative. This study empirically investigates the effectiveness of traditional CRV with a variety of profiling approaches and machine learning techniques using repeated cross-validation. Results show that the traditional approach generally performs best both in terms of predicting performance and larger group differences between candidates identified as top or non-top performers. In addition to empirical effectiveness, other practical implications are discussed

X-ray Properties of the Abell 644 Cluster of Galaxies

We use new ASCA observations and archival ROSAT Position Sensitive Proportional Counter (PSPC) data to determine the X-ray spectral properties of the intracluster gas in Abell 644. From the overall spectrum, we determine the average gas temperature to be 8.64 (+0.67,-0.56) keV, and an abundance of 0.32 (+/-0.04) $Z_{\odot}$. The global ASCA and ROSAT spectra imply a cooling rate of 214 (+100,-91) $M_{\odot}$ yr$^{-1}$. The PSPC X-ray surface brightness profile and the ASCA data suggest a somewhat higher cooling rate. We determine the gravitational mass and gas mass as a function of radius. The total gravitating mass within 1.2 Mpc is $6.2\times10^{14}$ $M_{\odot}$, of which 20% is in the form of hot gas. There is a region of elevated temperature 1.5-5 arcmin to the west of the cluster center. The south-southwest region of the cluster also shows excess emission in the ROSAT PSPC X-ray image, aligned with the major axis of the optical cD galaxy in the center of the cluster. We argue that the cluster is undergoing or has recently undergone a minor merger. The combination of a fairly strong cooling flow and evidence for a merger make this cluster an interesting case to test the disruption of cooling flow in mergers.Comment: 26 pages LaTeX including 9 eps figures + 4 pages LaTeX tables (landscape); accepted to ApJ, uses aaspp

Power laws, Discontinuities and Regional City Size Distributions

Urban systems are manifestations of human adaptation to the natural environment. City size distributions are the expression of hierarchical processes acting upon urban systems. In this paper, we test the entire city size distributions for the southeastern and southwestern United States (1990), as well as the size classes in these regions for power law behavior. We interpret the differences in the size of the regional city size distributions as the manifestation of variable growth dynamics dependent upon city size. Size classics in the city size distributions are snapshots of stable states within urban systems in flux

Time-series Analysis of Clusters in City Size Distributions

Complex systems, such as urban systems, emerge unpredictably without the influence of central control as a result of adaptive behavior by their component, interacting agents. This paper analyses city size distributions, by decade, from the south-western region of the United States for the years 1890–1990. It determines if the distributions were clustered and documents changes in the pattern of clusters over time. Clusters were determined utilizing a kernel density estimator and cluster analysis. The data were clustered as determined by both methods. The analyses identified 4–7 clusters of cities in each of the decades analysed. Cities cluster into size classes, suggesting variability in growth rates at different scales

Firm size diversity, functional richness, and resilience

This paper applies recent advances in ecology to our understanding of firm development, sustainability, and economic development. The ecological literature indicates that the greater the functional richness of species in a system, the greater its resilience – that is, its ability to persist in the face of substantial changes in the environment. This paper focuses on the effects of functional richness across firm size on the ability of industries to survive in the face of economic change. Our results indicate that industries with a richness of industrial functions are more resilient to employment volatility

The impact of artificial fragrances on the assessment of mate quality cues in body odor

We investigated the impact of artificial fragrances on the accurate detection of biologically relevant information in human body odor. To do this, we examined cross-sensory consistency (across faces and odors) in the perception of masculinity and femininity in men and women, and how consistency is influenced by the use of artificial fragrance. Independent sets of same and opposite-sex participants rated odor samples (with and without a fragrance, N = 239 raters), and photographs (N = 130) of 20 men and 20 women. In female, but not male raters, judgments of masculinity/femininity of non-fragranced odor and faces were correlated. However, the correlation between female ratings of male facial and odor masculinity was not evident when assessing a body odor and fragrance blend. Further analysis also indicated that differences in ratings of male odor masculinity between men with very masculine or high and low levels of facial masculinity were removed by the addition of fragrance. This effect was absent in ratings of female odors by both female and male raters, suggesting sex-specificity in the effects of fragrance on odor perception. The widespread use of artificial fragrance in many modern populations raises questions about how this cultural practice influences ability to detect and utilize mate-choice relevant cues. Our findings suggest that women may be more sensitive to these cues, and therefore also to disruption of this information through fragrance use. We discuss our results using the framework of culture-gene coevolution

Diffuse flow environments within basalt- and sediment-based hydrothermal vent ecosystems harbor specialized microbial communities

Hydrothermal vents differ both in surface input and subsurface geochemistry. The effects of these differences on their microbial communities are not clear. Here, we investigated both alpha and beta diversity of diffuse flow-associated microbial communities emanating from vents at a basalt-based hydrothermal system along the East Pacific Rise (EPR) and a sediment-based hydrothermal system, Guaymas Basin. Both Bacteria and Archaea were targeted using high throughput 16S rRNA gene pyrosequencing analyses. A unique aspect of this study was the use of a universal set of 16S rRNA gene primers to characterize total and diffuse flow-specific microbial communities from varied deep-sea hydrothermal environments. Both surrounding seawater and diffuse flow water samples contained large numbers of Marine Group I (MGI) Thaumarchaea and Gammaproteobacteria taxa previously observed in deep-sea systems. However, these taxa were geographically distinct and segregated according to type of spreading center. Diffuse flow microbial community profiles were highly differentiated. In particular, EPR dominant diffuse flow taxa were most closely associated with chemolithoautotrophs, and off axis water was dominated by heterotrophic-related taxa, whereas the opposite was true for Guaymas Basin. The diversity and richness of diffuse flow-specific microbial communities were strongly correlated to the relative abundance of Epsilonproteobacteria, proximity to macrofauna, and hydrothermal system type. Archaeal diversity was higher than or equivalent to bacterial diversity in about one third of the samples. Most diffuse flow-specific communities were dominated by OTUs associated with Epsilonproteobacteria, but many of the Guaymas Basin diffuse flow samples were dominated by either OTUs within the Planctomycetes or hyperthermophilic Archaea. This study emphasizes the unique microbial communities associated with geochemically and geographically distinct hydrothermal diffuse flow environments

Comparing Empirically Keyed and Random Forest Scoring Models in Biodata Assessments

Effective pre-hire assessments impact organizational outcomes. Recent developments in machine learning provide an opportunity for practitioners to improve upon existing scoring methods. This study compares the effectiveness of an empirically keyed scoring model with a machine learning, random forest model approach in a biodata assessment. Data was collected across two organizations. The data from the first sample (N=1,410), was used to train the model using sample sizes of 100, 300, 500, and 1,000 cases, whereas data from the second organization (N=524) was used as an external benchmark only. When using a random forest model, predictive validity rose from 0.382 to 0.412 in the first organization, while a smaller increase was seen in the second organization. It was concluded that predictive validity of biodata measures can be improved using a random forest modeling approach. Additional considerations and suggestions for future research are discussed

The auxiliary use of LANDSAT data in estimating crop acreages: Results of the 1975 Illinois crop-acreage experiment

The author has identified the following significant results. It was found that classifier performance was influenced by a number of temporal, methodological, and geographical factors. Best results were obtained when corn was tasselled and near the dough stage of development. Dates earlier or later in the growing season produced poor results. Atmospheric effects on results cannot be independently measured or completely separated from the effects due to the maturity stage of the crops. Poor classifier performance was observed in areas where considerable spectral confusion was present