A comparative study for the assessment of Ikonos satellite image-fusion techniques

I<span>Image-fusion provide users with detailed information about the urban and rural environment, which is useful for applications such as urban planning and management when higher spatial resolution images are not available. There are different image fusion methods. This paper implements, evaluates, and compares six satellite image-fusion methods, namely wavelet 2D-M transform, gram schmidt, high-frequency modulation, high pass filter (HPF) transform, simple mean value, and PCA. An Ikonos image (Panchromatic-PAN and multispectral-MULTI) showing the northwest of Bogotá (Colombia) is used to generate six fused images</span>: MULTI_{Wavelet 2D-M}, MULTI_G-S, MULTI_MHF, MULTI_HPF, MULTI_SMV, and MULTI_PCA. <span>In order to assess the efficiency of the six image-fusion methods, the resulting images were evaluated in terms of both spatial quality and spectral quality. To this end, four metrics were applied, namely the correlation index, erreur relative globale adimensionnelle de synthese (ERGAS), relative average spectral error (RASE) and the Q index. The best results were obtained for the </span> MULTI_SMV image, which exhibited spectral correlation higher than 0.85, a Q index of 0.84, and the highest scores in spectral assessment according to ERGAS and RASE, 4.36% and 17.39% respectively.

The Value of Anti-Müllerian Hormone in the Prediction of Spontaneous Pregnancy: A Systematic Review and Meta-Analysis

ObjectiveTo determine whether serum anti-Müllerian hormone (AMH) level is a predictor of clinical pregnancy in women trying to achieve a natural conception.MethodsThe PubMed, Embase, and Cochrane Library databases were searched for articles published until August 2020. Studies that met the inclusion and exclusion criteria were included in the meta-analysis; no language limitations were imposed. Quality was appraised using the Quality Assessment of Diagnostic Accuracy Studies-2 checklist. Heterogeneity due to the threshold effect was identified; thus, we plotted a summary receiver operating characteristic curve and calculated its area under the summary receiver operating characteristic curve (AUC) and Cochran’s Q index to assess whether AMH level is a predictor of spontaneous pregnancy. Publication bias and sensitivity were also assessed.ResultsEleven studies (4,388 women) were ultimately included in this meta-analysis. The AUC and Cochran’s Q indices were 0.5932 and 0.5702, respectively. For women younger than 35 years, the AUC was 0.6355 and the Q index was 0.6025. For those older than 35 years, the AUC was 0.5536 and the Q index was 0.5403. Subgroup analyses by study type and population characteristics showed results similar to the overall outcome. No publication bias was identified, and the sensitivity analysis confirmed the robustness of the final result.ConclusionsSerum AMH levels have poor predictive value for natural pregnancy. The predictive value of AMH was poor in the younger and older subgroups. Our findings suggest that low serum AMH levels are not associated with reduced fertility.IntroductionThis study investigated the predictive value of anti-Müllerian hormone (AMH) level for natural pregnancy. Other than age, few factors can predict the chances of natural fertility. AMH is an established biomarker of ovarian reserve that is widely used to predict oocyte yield in cases of in vitro fertilization (IVF) and menopause. In clinical practice, the applications of AMH are increasing. However, its predictive value for natural conception remains controversial. In this study, since AMH is closely related with ovarian reserve, we evaluated whether it has predictive value for natural pregnancy. Our findings will fine-tune the clinical application of AMH in pre-pregnancy counseling. The topic should be of wide interest to investigators in the reproductive endocrinology and gynecology fields.Systematic Review RegistrationPROSPERO 2020 CRD42020216265, Available from: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020216265.

Testing the Height Variation Hypothesis with the R rasterdiv Package for Tree Species Diversity Estimation

Forest biodiversity is a key element to support ecosystem functions. Measuring biodiversity is a necessary step to identify critical issues and to choose interventions to be applied in order to protect it. Remote sensing provides consistent quality and standardized data, which can be used to estimate different aspects of biodiversity. The Height Variation Hypothesis (HVH) represents an indirect method for estimating species diversity in forest ecosystems from the LiDAR data, and it assumes that the higher the variation in tree height (height heterogeneity, HH), calculated through the ’Canopy Height Model’ (CHM) metric, the more complex the overall structure of the forest and the higher the tree species diversity. To date, the HVH has been tested exclusively with CHM data, assessing the HH only with a single heterogeneity index (the Rao’s Q index) without making use of any moving windows (MW) approach. In this study, the HVH has been tested in an alpine coniferous forest situated in the municipality of San Genesio/Jenesien (eastern Italian Alps) at 1100 m, characterized by the presence of 11 different tree species (mainly Pinus sylvestris, Larix decidua, Picea abies followed by Betula alba and Corylus avellana). The HH has been estimated through different heterogeneity measures described in the new R rasterdiv package using, besides the CHM, also other LiDAR metrics (as the percentile or the standard deviation of the height distribution) at various spatial resolutions and MWs (ALS LiDAR data with mean point cloud density of 2.9 point/m2). For each combination of parameters, and for all the considered plots, linear regressions between the Shannon’s H′ (used as tree species diversity index based on field data) and the HH have been derived. The results showed that the Rao’s Q index (singularly and through a multidimensional approach) performed generally better than the other heterogeneity indices in the assessment of the HH. The CHM and the LiDAR metrics related to the upper quantile point cloud distribution at fine resolution (2.5 m, 5 m) have shown the most important results for the assessment of the HH. The size of the used MW did not influence the general outcomes but instead, it increased when compared to the results found in the literature, where the HVH was tested without MW approach. The outcomes of this study underline that the HVH, calculated with certain heterogeneity indices and LiDAR data, can be considered a useful tool for assessing tree species diversity in considered forest ecosystems. The general results highlight the strength and importance of LiDAR data in assessing the height heterogeneity and the related biodiversity in forest ecosystems.

Earnings Management, Related Party Transactions and Corporate Performance: The Moderating Role of Internal Control

The primary purpose of this study is to investigate the impacts of earnings management (EM) and related party transactions (RPTs) on corporate financial performance in an emerging market, Iran. This paper also aims to examine the moderating role of internal control weakness (ICW) in the relationship between them. The study sample includes 108 Iranian manufacturing companies listed on the Tehran Stock Exchange (TSE) between 2013 and 2018, and panel data with random effects are used to test the hypotheses. When an accounting-based measure called ROA is defined as a proxy for corporate performance, the results show that there is a negative association between real earnings management (REM) and corporate financial situation, while accrual-based earnings management (AEM) and firm value are correlated positively. However, when Tobin’s Q index is defined as a proxy for corporate performance, we do not find any significant association between them. Consistent with the tunneling hypothesis or agency theory, our findings confirm RPTs damage corporate value (ROA and Tobin’s Q) because managers probably consider it a mechanism to exploit enterprise resources owing to existing conflictual interests. Moreover, purchase-related party transactions lead to lower ROA, whereas sale-related party transactions and Tobin’s Q are correlated negatively. Moreover, weak internal control has a positive moderating influence on the linkage between AEM and Tobin’s Q index. Finally, we provide robust evidence that there is a positive association between sale growth and institutional owners with ROA and Tobin’s Q, although financial leverage and mergers and acquisitions (M&A) have a destructive effect on corporate value.

Tsallis q-Stat and the Evidence of Long-Range Interactions in Soil Temperature Dynamics

The complexities in the variations of soil temperature and thermal diffusion poses a physical problem that requires more understanding. The quest for a better understanding of the complexities of soil temperature variation has prompted the study of the q-statistics in the soil temperature variation with the view of understanding the underlying dynamics of the temperature variation and thermal diffusivity of the soil. In this work, the values of Tsallis stationary state q index known as q-stat were computed from soil temperature measured at different stations in Nigeria. The intrinsic variations of the soil temperature were derived from the soil temperature time series by detrending method to extract the influences of other types of variations from the atmosphere. The detrended soil temperature data sets were further analysed to fit the q-Gaussian model. Our results show that our datasets fit into the Tsallis Gaussian distributions with lower values of q-stat during rainy season and around the wet soil regions of Nigeria and the values of q-stat obtained for monthly data sets were mostly in the range 1.2≤q≤2.9 for all stations, with very few values q closer to 1.2 for a few stations in the wet season. The distributions obtained from the detrended soil temperature data were mostly found to belong to the class of asymmetric q-Gaussians. The ability of the soil temperature data sets to fit into q-Gaussians might be due and the non-extensive statistical nature of the system and (or) consequently due to the presence of superstatistics. The possible mechanisms responsible this behaviour was further discussed.

Empirical Support Design for Proposed Diversion Tunnels at Dasu Dam Site Pakistan

This research work presents the rock mass characteristics and tunnel support system recommendations for hydroelectric power tunnels at Dasu dam site Pakistan. Two inverted U-shaped tunnels are proposed at the left bank of Indus river. The tunnels have inlet portals at an elevation of 773.00 m and outlet portals at an elevation of 758.00 m. The thickness of rock cover above the tunnels is between 100 and 200 m. Three types of rock are encountered at project site including Granulite, Amphibolite and Gabbronorite. Granulite rocks are encountered along the alignment of tunnels. Rock mass is classified using Rock mass rating (RMR) and Tunneling quality index (Q system). Support system is suggested based on values of Q and RMR. Correlation between Q-index and RMR is also derived.

Evaluating the Impact of Multidimensionality on Type I and Type II Error Rates Using the Q-Index Item Fit Statistic for the Rasch Model

To understand the role of fit statistics in Rasch measurement is simple: applied researchers can only benefit from the desirable properties of the Rasch model when the data fit the model. The purpose of the current study was to assess the Q-Index robustness (Ostini and Nering, 2006), and its performance was compared to the current popular fit statistics known as MSQ Infit, MSQ Outfit, and standardized Infit and Outfit (ZSTDs) under varying conditions of test length, sample size, item difficulty (normal and uniform), and dimensionality utilizing a Monte Carlo simulation. The Type I and Type II error rates are also examined across fit indices. This study provides applied researchers guidelines the robustness and appropriateness of the use of the Q-Index, which is an alternative to the currently available item fit statistics. The Q-Index was slightly more sensitive to the levels of multidimensionality set in the study while MSQ Infit, Outfit, and standardized Infit and Outfit (ZSTDs) failed to identify the multidimensional conditions. The Type I error rate of the Q-Index was lower than the rest of the fit indices; however, the Type II error rate was higher than the anticipated β=.20 across all fit indices.