Low Level Of Significance Research Articles

Landslide hazard mapping of Ibrahim River Basin, Lebanon

Landslide susceptibility and hazard maps were established using an adapted Value Analytical Bi-Univariate (VABU) method for Nahr Ibrahim watershed located in Mount Lebanon. The site covered an area of 312 km2 compromising 3 % of Lebanon. Pan-sharpened IKONOS imageries were used to delineate landslides. Morphological, geological soil, hydrological and anthropic parametric factors affecting landslides were extracted under GIS environment. Statistical correlation between existing landslides and related factors using both univariate and bivariate analysis was also conducted. The latter along with landslide and landslide preconditioning and triggering factor databases enabled establishing the landslide susceptibility and hazard maps for the area. The visual interpretation of the satellite imageries has allowed the delineation of 229 landslides covering an area of 7.6 km2. The study indicates, depending on bivariate remote sensing and GIS statistical correlations (Kendall tau-b correlation), that lithology is the most influencing on landslide occurrence, having the highest correlation with other parameters (i.e., 7 times correlated at 1 % level of significance and 3 times at 5 %). It also shows that statistical correlations with landslide exist best between other parameters but with lower levels of significance. The proposed mathematical decision-making method (VABU) that considered two-level weights for mapping MM susceptibility/hazard showed that 19 and 7 % of the watershed are identified as very high susceptibility and hazard areas, respectively. The accuracy of the VABU model was checked and proved to be approximately 74 % in both maps.

P-Values: Misunderstood and Misused

P-values are widely used in both the social and natural sciences to quantify the statistical significance of observed results. The recent surge of big data research has made the p-value an even more popular tool to test the significance of a study. However, substantial literature has been produced critiquing how p-values are used and understood. In this paper we review this recent critical literature, much of which is routed in the life sciences, and consider its implications for social scientific research. We provide a coherent picture of what the main criticisms are, and draw together and disambiguate common themes. In particular, we explain how the False Discovery Rate is calculated, and how this differs from a p-value. We also make explicit the Bayesian nature of many recent criticisms, a dimension that is often underplayed or ignored. We conclude by identifying practical steps to help remediate some of the concerns identified. We recommend that (i) far lower significance levels are used, such as $0.01$ or $0.001$, and (ii) p-values are interpreted contextually, and situated within both the findings of the individual study and the broader field of inquiry (through, for example, meta-analyses).

全方位緑視率を用いた緑地環境に対する印象評価推定モデル作成と検証

In recent years, there have been a number of social initiatives related to improving the environment in city landscapes. Green space is becoming a tool to enhance the comfort of city space. "The Basic Plan for Green of Kyoto City" is one such example, where the ratio of visible green space is being used as a tool to improve the city environment. Many studies are being carried out to support this initiative and this study is one of them. The purpose of this study was to: 1) present a method to measure the location/angle specific ratio of green spaces in the omnidirectional visibility rate using a three-dimensional model of the target location, 2) create a perception deduction model based on Self-organizing Maps and 72 variables of visible green space in omnidirectional visibility rate, and 3) statistical verification of the accuracy of the perception deduction model. There are 72 categories of green space in the omnidirectional visibility rate. These categories are based on the location- and angle specific ratio of these spaces. Six of these categories were used for the location specific measurement, namely, "tall trees", "medium trees", "shrubs", " implantable ", "ground cover", and "others". Twelve angle specific measurements for every fifteen degrees were used and eight perception estimation parameters were selected. The perception estimation parameters included: “many or less", "satisfied or not satisfied", "pleasant", "serene", "covered (wrapped)", "close by or far", " surrounded by", "refreshing” and “widely spread". In this paper, we present results from the "ratio of visible green space in the omnidirectional visibility rate map”, the “self-organizing map" and the "perception estimation value map”. During the verification of the perception estimation model (the primary objective of this study), we compared the estimated perception values with the survey based observed values associated with a location of green space that was not included in the model creation. When we compared them statistically, we confirmed a significant correlation (n=32, p<0.05) between the estimated values and observed values (Pearson's correlation). We noted that the strength of the correlation was moderate but significant (correlation coefficient values around 0.6), with when we used the lower significance level (p<0.001). Taking into account effect size from psychological statistics, the average difference between the estimated and observed values of perception can be considered small for the parameters "many or less" "satisfied or not satisfied", "pleasant", "serene", "covered (wrapped)", "close by or far" and" surrounded by". However, the average difference was moderate for “Refreshing” and “Widely spread” and a significant difference between observed and estimated perception values was noted for these parameters in a paired t-test. Consequently, this perception deduction model is able to predict low and high values of “Refreshing” and “Widely spread", however, we need to be aware of the one degree difference, which happens to be the width of the confidence interval and may affect the estimated values.

Estimation of Moisture Content in Comminuted Miscanthus based on the Intensity of Reflected Light

Purpose: The balance between miscanthus production and its cost effectiveness depends greatly on its moisture content during post processing. The objective of this research was to measure the moisture content using a non-destructive and non-contact methodology for in situ applications. Methods: The moisture content of comminuted miscanthus was controlled using a closed chamber, a humidifier, a precision weigher, and a real-time monitoring software developed in this research. A CMOS sensor equipped with magnifier lens was used to capture magnified images of the conditioned materials with moisture content level from 5 to 30%. The hypothesis is that when light is incident on the comminuted particles in an inclined manner, higher moisture content results in light being reflected with a higher intensity. Results: A linear regression analysis for an initiative hypothesis based on general histogram analysis yielded insufficient correlations with low significance level (

Sharia, Piety and Maslak: Support for Sectarian Violence in Pakistan

In the discourse around sectarian violence in Pakistan, two concerns are prominent. The first is the notion of piety or the intensity of Muslim religious practice as a potential predictor for sectarian and other forms of Islamist violence. The second is the belief that support for some forms of sharia predict this support. As I describe herein, these notions first manifested in the “clash of civilizations” thesis and developed further in the scholarly and policy analytical literatures that sought to explore these linkages through qualitative and quantitative methodologies. In this paper, I use dataset derived from a recent and large national survey of Pakistanis. I use various questions to create indices of both piety as well as support for three dimensions of Sharia, described herein. I use these indices as explanatory variables, along with other explanatory and control variables such as sectarian background, in my regression analysis of support for sectarian violence, my dependent variable. I find that the piety index is a positive predictor of support for sectarian terrorism. at low levels of statistical significance (p

TheGaia-ESO Survey: Detailed abundances in the metal-poor globular cluster NGC 4372

We present the abundance analysis for a sample of 7 red giant branch stars in the metal-poor globular cluster NGC 4372 based on UVES spectra acquired as part of the Gaia-ESO Survey. This is the first extensive study of this cluster from high resolution spectroscopy. We derive abundances of O, Na, Mg, Al, Si, Ca, Sc, Ti, Fe, Cr, Ni, Y, Ba, and La. We find a metallicity of [Fe/H] = -2.19 $\pm$ 0.03 and find no evidence for a metallicity spread. This metallicity makes NGC 4372 one of the most metal-poor galactic globular clusters. We also find an {\alpha}-enhancement typical of halo globular clusters at this metallicity. Significant spreads are observed in the abundances of light elements. In particular we find a Na-O anti-correlation. Abundances of O are relatively high compared with other globular clusters. This could indicate that NGC 4372 was formed in an environment with high O for its metallicity. A Mg-Al spread is also present which spans a range of more than 0.5 dex in Al abundances. Na is correlated with Al and Mg abundances at a lower significance level. This pattern suggests that the Mg-Al burning cycle is active. This behavior can also be seen in giant stars of other massive, metal-poor clusters. A relation between light and heavy s-process elements has been identified.

Improving Business Cycle Forecasts' Accuracy - What Can We Learn from Past Errors?

This paper addresses the question whether forecasters could have been able to produce better forecasts by using the available information more efficiently (informational efficiency of forecast). It is tested whether forecast errors covariate with indicators such as survey results, monetary data, business cycle indicators, or financial data. Because of the short sampling period and data problems, a non parametric ranked sign test is applied. The analysis is carried out for GDP and its main components. The study differentiates between two types of errors: Type I error occurs when forecasters neglect the information provided by an indicator. As type II error a situation is labelled in which forecasters have given too much weight to an indicator. In a number of cases forecast errors and the indicators are correlated, though mostly at a rather low level of significance. In most cases type I errors have been found. Additional tests reveal that there is little evidence of institution specific as well as forecast horizon specific effects. In many cases, co-variations found for GDP are not refected in one of the expenditure side components et vice versa.

Factors associated with physical or sexual intimate partner violence perpetration by men attending substance misuse treatment in Catalunya: A mixed methods study.

Prevalence of intimate partner violence perpetration is higher among male substance misusers than men in the general population. Previous studies have included few risk factors, limiting their capacity to inform interventions. The aim of this study was to examine factors associated with intimate partner violence by male substance misusers. Two hundred and thirty-five men in treatment for substance misuse completed surveys that included the Revised Conflict Tactics Scale and the Psychological Maltreatment of Women Inventory (PMWI). Variables significant in bivariate analyses were entered into multiple logistic regression analyses. Seventeen in-depth interviews were conducted with perpetrators and analysed using a framework approach. Just over a third of the men (34%) had been violent in the last year to their current/most recent partner. After excluding the men's own domestic victimisation from the multivariate model, perpetratation of such violence was significantly and independently associated with lower level of education, having higher PMWI dominance-isolation and emotional-verbal subscale scores and parents who had separated/divorced, and at a lower level of significance, childhood physical abuse, hazardous drinking and cocaine as the principal drug for which treatment was sought. Interview data suggested that perpetrators 'blamed' alcohol or cocaine use, jealousy, control and provocation or 'fighting back' for their behaviour. Intimate partner violence is common among men attending substance misuse treatment. Integrated interventions should that address both intimate partner violence and substance misuse should be considered. Areas for intervention would include reducing dominating-isolating behaviours and emotional-verbal abuse, improving communication skills, challenging gender-specific roles and believing that substance use 'causes' violent behaviour.

Evidence against a strong thermal inversion in HD 209458b from high-dispersion spectroscopy

Broadband secondary-eclipse measurements of hot Jupiters have indicated the existence of atmospheric thermal inversions, but their presence is difficult to determine from broadband measurements because of degeneracies between molecular abundances and temperature structure. We apply high-resolution (R = 100 000) infrared spectroscopy to probe the temperature-pressure profile of HD 209458 b. This bright, transiting hot-Jupiter has long been considered the gold standard for a hot Jupiter with an inversion layer, but this has been challenged in recent publications. We observed the thermal dayside emission of HD 209458 b with CRIRES / VLT during three nights, targeting the carbon monoxide band at 2.3 microns. Thermal inversions give rise to emission features, which means that detecting emission lines in the planetary spectrum, as opposed to absorption lines, would be direct evidence of a region in which the temperature increases with altitude. We do not detect any significant absorption or emission of CO in the dayside spectrum of HD 209458 b, although cross-correlation with template spectra either with CO absorption lines or with weak emission at the core of the lines show a low-significance correlation signal with a signal-to-noise ratio of 3 - 3.5. Models with strong CO emission lines show a weak anti-correlation with similar or lower significance levels. Furthermore, we found no evidence of absorption or emission from H2O at these wavelengths. The non-detection of CO in the dayside spectrum of HD 209458 b is interesting in light of a previous CO detection in the transmission spectrum. That there is no signal indicates that HD 209458 b either has a nearly isothermal atmosphere or that the signal is heavily muted. Assuming a clear atmosphere, we can rule out a full-disc dayside inversion layer in the pressure range 1 bar to 1 mbar.

Prediction Method for Daylight Illuminance of Photosensor in a Small Office

This study proposes a simulation method for effective prediction of photosensor illuminance under various daylight conditions. Computational theory for simulation was developed and prediction results by the simulations were validated against field measurement results. Results indicate that difference range between simulation and measurement was wide when direct component of daylight was strong. The absolute difference range under overcast sky was narrow compared with that under clear and partly cloudy sky. Correlation between simulated and measured illuminance was strong. Linear relationship between them was strongest under clear sky and weakest under overcast sky conditions. Regression models for the difference was acceptable under a low significance level.

A variance inflation factor and backward elimination based robust regression model for forecasting monthly electricity demand using climatic variables

Selection of appropriate climatic variables for prediction of electricity demand is critical as it affects the accuracy of the prediction. Different climatic variables may have different impacts on the electricity demand due to the varying geographical conditions. This paper uses multicollinearity and backward elimination processes to select the most appropriate variables and develop a multiple regression model for monthly forecasting of electricity demand. The former process is employed to reduce the collinearity between the explanatory variables by excluding the predictor which has highly linear relationship with the other independent variables in the dataset. In the next step, involving backward elimination regression analysis, the variables with coefficients that have a low level of significance are removed. A case study has been reported in this paper by acquiring the data from the state of New South Wales, Australia. The data analyses have revealed that the climatic variables such as temperature, humidity, and rainy days predominantly affect the electricity demand of the state of New South Wales. A regression model for monthly forecasting of the electricity demand is developed using the climatic variables that are dominant. The model has been trained and validated using the time series data. The monthly forecasted demands obtained using the proposed model are found to be closely matched with the actual electricity demands highlighting the fact that the prediction errors are well within the acceptable limits.

Recombination analysis of Maize dwarf mosaic virus (MDMV) in the Sugarcane mosaic virus (SCMV) subgroup of potyviruses

Recombination among RNA viruses is a natural phenomenon that appears to have played a significant role in the species development and the evolution of many strains. It also has particular significance for the risk assessment of plants which have been genetically modified for disease resistance by incorporating viral sequences into their genomes. However, the exact recombination events taking place in viral genomes are not investigated in detail for many virus groups. In this analysis, different single-stranded positive-sense RNA potyviruses were compared using various in silico recombination detection methods and new recombination events in the Sugarcane mosaic virus (SCMV) subgroup were detected. For an extended in silico recombination analysis, two of the analyzed Maize dwarf mosaic virus full-length genomes were sequenced additionally during this work. These results strengthen the evidence that recombination is a major driving force in virus evolution, and the emergence of new virus variants in the SCMV subgroup, paired with mutations, could generate viruses with altered biological properties. The intra- and interspecific homolog recombinations seem to be a general trait in this virus group, causing little or no changes to the amino acid of the progenies. However, we found a few breakpoints between the members of SCMV subgroup and the weed-infecting distant relatives, but only a few methods of the RDP3 package predicted these events with low significance level.

Identificação de Características das Cooperativas Agropecuárias que Influenciam a Geração de Valor Adicionado

This research aims to determine the strategic variables of adding value to influence the generation of value added of agricultural cooperatives. For this, we used the mean difference of the different strategies for adding value, MannWhitney and Kruskal-Wallis tests, with data from 76 agricultural cooperatives in the state of Rio Grande do Sul from 2011 to 2012. As a result, it was observed that the characteristics that affect the value added cooperatives are the presence of agribusiness, the brand itself and present the main soy product. However, when the tests are performed with the value added per cooperative, only the presence of own brand proved to be statistically different, though at a lower level of significance. Evaluating strategic variable region, the results are presented in a manner contrary, since the value added showed no statistically different between regions, but when the tests are performed with the value added by cooperating can verify significant differences.

Novel genotype–phenotype associations demonstrated by high-throughput sequencing in patients with hypertrophic cardiomyopathy

ObjectiveA predictable relation between genotype and disease expression is needed in order to use genetic testing for clinical decision-making in hypertrophic cardiomyopathy (HCM). The primary aims of this study were...

The (Possible) Effect of Plain Packaging on Smoking Prevalence in Australia: A Trend Analysis

A stated objective of the Australian Plain Packaging Act 2011 is to reduce smoking prevalence. We use the Roy Morgan Single Source (Australia) data set over the time period January 2001 to December 2013 to analyze whether this goal has been achieved in the first year since the implementation. In particular, we carry out a statistical trend analysis to study the (possible) effect of plain packaging on smoking prevalence. Two informative analyses help to draw conclusions on the (actual) effect of plain packaging on smoking prevalence in Australia. First, we look at the year of data before plain packaging was introduced, which happened in December 2012. Second, we compute confidence intervals around the estimated treatment effects. Our main results can be summarized as follows. First, if a statistical significance level of 5% is required, then there is no evidence at all for a plain packaging effect on smoking prevalence. Second, if one is willing to accept a relatively low level of statistical significance (that is, 10%), then there is evidence for a very short-lived plain packaging effect on smoking prevalence, namely in December 2012 only (after which smoking prevalence is statistically indistinguishable from its pre-existing trend). A formal power analysis demonstrates that the power of our inference methods is remarkably high.

0026 Psychosocial Stress of nurses in oncology: Effort-Reward Imbalance Scale

ObjectivesTo identify sociodemographic variables associated with psychosocial stress in oncology nurses;Assess the associations between ocupational variables and stress in the work environment of nurses in oncology;Discuss the impact of psychosocial...

Detection of five substitution TPO mutations in Polycystic Ovary Syndrome (PCOS) and thyroid hormones disturbance patients

Polycystic ovary syndrome (PCOS (is a complex disorder which reflects variable clinical symptoms. There is considerable heterogeneity of symptoms and signs among women with PCOS, and for an individual, these may change over time. The genetic base of PCOS is not clear and no concrete genetic correlation was built with PCOS. The current study showed the presence of multiple peripheral small cysts 5-9 mm also there was an increase in ovarian volume 3.7-3.9 cm and change in ovarian dimensions with the ovary being more spherical. 20.8% of the PCOS patients included in this study found to have hyperthyroidism. Thethyroid stimulating hormone-TSH level was significantly higher 17.34±5.12μIU/ml in 4 POCS patients with thyroid hormones disturbance than the level in POCS patients without thyroid disturbance or healthy control group )2.19 ±0.47 and 2.33±0.44 μIU/ml respectively(. On the other hand, the rest of POCS patients with thyroid hormones disturbance 7 Patients showed lower significance levels of triiodothyronine-T3 and thyroxine-T4 (0.96±0.029 and 51.33± 10.96 n.mol/L respectively) than other groups. At the molecular analysis five substitution thyroid peroxidase genes -TPO mutations were detected in 5 patients with PCOS and thyroid hormones disturbances. Three of them were detected in exon 8 and two in exon 9. The missense substation mutations detected in this study involve one transition of T to C (c.904T&gt;C) and 4 transversion of C to G, C to A and G to C (c.904T&gt;C, c.1280C&gt;G, c.1265C&gt;A, c.1617G&gt;C and c.1603G&gt;C respectively).

Reply to comment by Laprise on “The added value to global model projections of climate change by dynamical downscaling: A case study over the continental U.S. using the GISS‐ModelE2 and WRF models”

In his comment, Laprise raises several points that we agree merit consideration. His primary critique is that our study [Racherla et al., 2012] tested the ability of the WRF regional climate model to reproduce historical temperature and precipitation change relative to the driving global climate model (GCM) using only a single simulation rather than an ensemble. He asserts that the observed changes are smaller than the internal variability in the climate system (i.e., not statistically significant) and that thus a single simulation should not necessarily be able to capture the observations. Laprise points out that the statistical signal is reduced for a multi-decadal trend such as the one we analyzed in comparison with mean climatology and cites two studies showing that for particular climate parameters it can take any years for a signal to be discerned over internal variability. He states that The results of theexperiment as designed were strongly influenced by the presence of internal variability and sampling errors,which masked the rather small climate changes that may have occurred as a consequence of changes inforcing during the period considered. While Laprise discusses statistics in general terms at some length, for the actual climate trends examined in our study, he offers no evidence that the forced signal was smallcompared with internal variability. The two studies he cites [de Ela et al., 2013; Maraun, 2013] do not provide convincing evidence as they concern climate variables averaged over different times and areas. One in fact examines extreme precipitation events, which by definition are rare and thus have a lower significance level. We accept the general point that it is important to consider internal variability, and as noted in our paper we agree that an ensemble of simulations is in principle an optimal, though computationally expensive, approach. While we did not present the statistical significance of the observations in our original paper, we have now evaluated those for the regional temperature trends used in our study to evaluate the added value of WRF and thus can analyze data as to the magnitude of the trends with respect to internal variability.

An Expression QTL of Closely Linked Candidate Genes Affects pH of Meat in Chickens

Genetical genomics has been suggested as a powerful approach to study the genotype-phenotype gap. However, the relatively low power of these experiments (usually related to the high cost) has hindered fulfillment of its promise, especially for loci (QTL) of moderate effects.One strategy with which to overcome the issue is to use a targeted approach. It has two clear advantages: (i) it reduces the problem to a simple comparison between different genotypic groups at the QTL and (ii) it is a good starting point from which to investigate downstream effects of the QTL. In this study, from 698 F2 birds used for QTL mapping, gene expression profiles of 24 birds with divergent homozygous QTL genotypes were investigated. The targeted QTL was on chromosome 1 and affected initial pH of breast muscle. The biological mechanisms controlling this trait can be similar to those affecting malignant hyperthermia or muscle fatigue in humans. The gene expression study identified 10 strong local signals that were markedly more significant compared to any genes on the rest of the genome. The differentially expressed genes all mapped to a region <1 Mb, suggesting a remarkable reduction of the QTL interval. These results, combined with analysis of downstream effect of the QTL using gene network analysis, suggest that the QTL is controlling pH by governing oxidative stress. The results were reproducible with use of as few as four microarrays on pooled samples (with lower significance level). The results demonstrate that this cost-effective approach is promising for characterization of QTL.

Barred S0 galaxies in the Coma cluster

This study uses r-band images from the Eighth Data Release of the Sloan Digital Sky Survey (SDSS DR8) to study bars in lenticular (S0) galaxies in one of the nearest rich cluster environments, the Coma cluster. We develop techniques for bar detection and assess their success when applied to SDSS image data. To detect and characterize bars, we perform 2D bulge+disc+bar light decompositions of galaxy images with galfit. Using a sample of artificial galaxy images, we determine the faintest magnitude at which bars can be successfully measured at the depth and resolution of SDSS. We perform detailed decompositions of 83 S0 galaxies in Coma, 64 from a central sample, and 19 from a cluster outskirt sample. For the central sample, the S0 bar fraction is |$72^{+5}_{-6}$| per cent. This value is significantly higher than that obtained using an ellipse-fitting method for bar detection, |$48^{+6}_{-6}$| per cent. At a fixed luminosity, barred S0s are redder in g − r colour than unbarred S0s by 0.02 mag. The frequency and strength of bars increase towards fainter luminosities. Neither central metallicity nor stellar age distributions differ significantly between barred and unbarred S0s. There is an increase in the bar fraction towards the cluster core, but this is at a low significance level. Bars have at most a weak correlation with cluster-centric radius.