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Magnetic Resonance Imaging (MRI) is an important diagnostic scanning tool for the detection and monitoring of specific diseases and conditions. However, the equipment cost, maintenance and specialty training of the technologists make the examination expensive. Consequently, unnecessary scanner time caused by poor scheduling, repeated sequences, aborted sequences, scanner idleness, or capture of non-diagnostic or low-value sequences is an opportunity to reduce costs and increase efficiency. This paper analyzes data collected from log files on 29 scanners over several years. 'Wasted' time is defined and key performance indicators (KPIs) are identified. A decrease in exam duration results when actively modifying and monitoring the number of sequences that comprise the exam card for a protocol. [ABSTRACT FROM AUTHOR]

Background: Time series analysis is critical for understanding brain signals and their relationship to behavior and cognition. Cluster-based permutation tests (CBPT) are commonly used to analyze a variety of electrophysiological signals including EEG, MEG, ECoG, and sEEG data without a priori assumptions about specific temporal effects. However, two major limitations of CBPT include the inability to directly analyze experiments with multiple fixed effects and the inability to account for random effects (e.g. variability across subjects). Here, we propose a flexible multi-step hypothesis testing strategy using CBPT with Linear Mixed Effects Models (LMEs) and Generalized Linear Mixed Effects Models (GLMEs) that can be applied to a wide range of experimental designs and data types. Methods: We first evaluate the statistical robustness of LMEs and GLMEs using simulated data distributions. Second, we apply a multi-step hypothesis testing strategy to analyze ERPs and broadband power signals extracted from human ECoG recordings collected during a simple image viewing experiment with image category and novelty as fixed effects. Third, we assess the statistical power differences between analyzing signals with CBPT using LMEs compared to CBPT using separate t-tests run on each fixed effect through simulations that emulate broadband power signals. Finally, we apply CBPT using GLMEs to high-gamma burst data to demonstrate the extension of the proposed method to the analysis of nonlinear data. Results: First, we found that LMEs and GLMEs are robust statistical models. In simple simulations LMEs produced highly congruent results with other appropriately applied linear statistical models, but LMEs outperformed many linear statistical models in the analysis of “suboptimal” data and maintained power better than analyzing individual fixed effects with separate t-tests. GLMEs also performed similarly to other nonlinear statistical models. Second, in real world human ECoG data, LMEs performed at least as well as separate t-tests when applied to predefined time windows or when used in conjunction with CBPT. Additionally, fixed effects time courses extracted with CBPT using LMEs from group-level models of pseudo-populations replicated latency effects found in individual category-selective channels. Third, analysis of simulated broadband power signals demonstrated that CBPT using LMEs was superior to CBPT using separate t-tests in identifying time windows with significant fixed effects especially for small effect sizes. Lastly, the analysis of high-gamma burst data using CBPT with GLMEs produced results consistent with CBPT using LMEs applied to broadband power data. Conclusions: We propose a general approach for statistical analysis of electrophysiological data using CBPT in conjunction with LMEs and GLMEs. We demonstrate that this method is robust for experiments with multiple fixed effects and applicable to the analysis of linear and nonlinear data. Our methodology maximizes the statistical power available in a dataset across multiple experimental variables while accounting for hierarchical random effects and controlling FWER across fixed effects. This approach substantially improves power leading to better reproducibility. Additionally, CBPT using LMEs and GLMEs can be used to analyze individual channels or pseudo-population data for the comparison of functional or anatomical groups of data.

Descriptions of how temperature patterns change through time can be relevant in light of the present worry about climate change. These trends can be deduced from monthly temperature data collected over the past few decades. To model temperature patterns, generalized linear models are frequently utilized. These models can represent temperature variations over the course of a year, but they have limitations when it comes to predicting long-term trends, especially when they are non-linear. By fitting smooth functions to the data, generalized additive models (GAMs) provide a framework for modeling non-linear connections. Using data from the Department of Meteorology and Climatology University of Jos from January 1986 to December 2023, this research illustrates how GAMs can enhance the flexibility of models to reflect seasonal patterns and longterm trends in temperature. Smoothed model estimations provide useful graphical depictions of a rise in temperature patterns at this area during the previous 38 years. GAMs are very useful for looking for nonlinear correlations in data. Smooth functions must be chosen with care to ensure that they are appropriate for the data and modeling goals. [ABSTRACT FROM AUTHOR]

Microdata are required to evaluate the distributive impact of the taxation system as a whole (direct and indirect taxes) on individuals or households. However, in European Union countries this information is usually distributed into two separate surveys: the Household Budget Surveys (HBS), including total household expenditure and its composition, and EU Statistics on Income and Living Conditions (EU-SILC), including detailed information about households' income and direct (but not indirect) taxes paid. We present a parametric statistical matching procedure to merge both surveys. For the first stage of matching, we propose estimating total household expenditure in HBS (Engel curves) using a GLM estimator, instead of the traditionally used OLS method. It is a better alternative, insofar as it can deal with the heteroskedasticity problem of the OLS estimates, while making it unnecessary to retransform the regressors estimated in logarithms. To evaluate these advantages of the GLM estimator, we conducted a computational Monte Carlo simulation. In addition, when an error term is added to the deterministic imputation of expenditure in the EU-SILC, we propose replacing the usual Normal distribution of the error with a Chi-square type, which allows a better approximation to the original expenditures variance in the HBS. An empirical analysis is provided using Spanish surveys for years 2012–2016. In addition, we extend the empirical analysis to the rest of the European Union countries, using the surveys provided by Eurostat (EU-SILC, 2011; HBS, 2010). [ABSTRACT FROM AUTHOR]

In a recent paper, Baño-Medina et al. (Configuration and Intercomparison of deep learning neural models for statistical downscaling. preprint, 2019) assessed the suitability of deep convolutional neural networks (CNNs) for downscaling of temperature and precipitation over Europe using large-scale 'perfect' reanalysis predictors. They compared the results provided by CNNs with those obtained from a set of standard methods which have been traditionally used for downscaling purposes (linear and generalized linear models), concluding that CNNs are well suited for continental-wide applications. That analysis is extended here by assessing the suitability of CNNs for downscaling future climate change projections using Global Climate Model (GCM) outputs as predictors. This is particularly relevant for this type of "black-box" models, whose results cannot be easily explained based on physical reasons and could potentially lead to implausible downscaled projections due to uncontrolled extrapolation artifacts. Based on this premise, we analyze in this work the two key assumptions that are made in perfect prognosis downscaling: (1) the predictors chosen to build the statistical model should be well reproduced by GCMs and (2) the statistical model should be able to reliably extrapolate out of sample (climate change) conditions. As a first step to test the suitability of these models, the latter assumption is assessed here by analyzing how the CNNs affect the raw GCM climate change signal (defined as the difference, or delta, between future and historical climate). Our results show that, as compared to well-established generalized linear models (GLMs), CNNs yield smaller departures from the raw GCM outputs for the end of century, resulting in more plausible downscaling results for climate change applications. Moreover, as a consequence of the automatic treatment of spatial features, CNNs are also found to provide more spatially homogeneous downscaled patterns than GLMs. [ABSTRACT FROM AUTHOR]

We study the law of the iterated logarithm (LIL) for the maximum likelihood estimation of the parameters (as a convex optimization problem) in the generalized linear models with independent or weakly dependent (ρ-mixing) responses under mild conditions. The LIL is useful to derive the asymptotic bounds for the discrepancy between the empirical process of the log-likelihood function and the true log-likelihood. The strong consistency of some penalized likelihood-based model selection criteria can be shown as an application of the LIL. Under some regularity conditions, the model selection criterion will be helpful to select the simplest correct model almost surely when the penalty term increases with the model dimension, and the penalty term has an order higher than O(log log n) but lower than O(n). Simulation studies are implemented to verify the selection consistency of Bayesian information criterion. [ABSTRACT FROM AUTHOR]

• Combining CBPT with GLMEs allows statistical analysis to match experimental design. • CBPT with GLMEs accounts for subject variability and hierarchical random effects. • The proposed method maintains control of type I error, type II error, and FWER. • CBPT with GLMEs can be applied to individual channels and pseudo-population data. Time series analysis is critical for understanding brain signals and their relationship to behavior and cognition. Cluster-based permutation tests (CBPT) are commonly used to analyze a variety of electrophysiological signals including EEG, MEG, ECoG, and sEEG data without a priori assumptions about specific temporal effects. However, two major limitations of CBPT include the inability to directly analyze experiments with multiple fixed effects and the inability to account for random effects (e.g. variability across subjects). Here, we propose a flexible multi-step hypothesis testing strategy using CBPT with Linear Mixed Effects Models (LMEs) and Generalized Linear Mixed Effects Models (GLMEs) that can be applied to a wide range of experimental designs and data types. We first evaluate the statistical robustness of LMEs and GLMEs using simulated data distributions. Second, we apply a multi-step hypothesis testing strategy to analyze ERPs and broadband power signals extracted from human ECoG recordings collected during a simple image viewing experiment with image category and novelty as fixed effects. Third, we assess the statistical power differences between analyzing signals with CBPT using LMEs compared to CBPT using separate t-tests run on each fixed effect through simulations that emulate broadband power signals. Finally, we apply CBPT using GLMEs to high-gamma burst data to demonstrate the extension of the proposed method to the analysis of nonlinear data. First, we found that LMEs and GLMEs are robust statistical models. In simple simulations LMEs produced highly congruent results with other appropriately applied linear statistical models, but LMEs outperformed many linear statistical models in the analysis of "suboptimal" data and maintained power better than analyzing individual fixed effects with separate t-tests. GLMEs also performed similarly to other nonlinear statistical models. Second, in real world human ECoG data, LMEs performed at least as well as separate t-tests when applied to predefined time windows or when used in conjunction with CBPT. Additionally, fixed effects time courses extracted with CBPT using LMEs from group-level models of pseudo-populations replicated latency effects found in individual category-selective channels. Third, analysis of simulated broadband power signals demonstrated that CBPT using LMEs was superior to CBPT using separate t-tests in identifying time windows with significant fixed effects especially for small effect sizes. Lastly, the analysis of high-gamma burst data using CBPT with GLMEs produced results consistent with CBPT using LMEs applied to broadband power data. We propose a general approach for statistical analysis of electrophysiological data using CBPT in conjunction with LMEs and GLMEs. We demonstrate that this method is robust for experiments with multiple fixed effects and applicable to the analysis of linear and nonlinear data. Our methodology maximizes the statistical power available in a dataset across multiple experimental variables while accounting for hierarchical random effects and controlling FWER across fixed effects. This approach substantially improves power leading to better reproducibility. Additionally, CBPT using LMEs and GLMEs can be used to analyze individual channels or pseudo-population data for the comparison of functional or anatomical groups of data. [ABSTRACT FROM AUTHOR]

West Bengal forest department in India was a pioneer in initiating Joint Forest Management (JFM) at West Midnapore involving participatory forest management by both foresters and local communities in order to protect degraded forests. The research objective was qualitative and quantitative study of tree communities by quadrat method along with identification of major impacts of environmental factors affecting regeneration of five locations under JFM in natural coppicing Sal (Shorea robusta) dominated tropical dry deciduous forests of West Bengal. 23 families, 33 genera and 36 tree species were identified. Dominant families were Anacardiaceae and Combretaceae. There were statistically significant differences in stand diversity, dominance, richness and evenness. Generalized Linear Model predictors such as site categories, seasons, grazing intensity and invasive species frequency had significant impacts on seedling diversity, dominance, richness and density. Surface soil potassium and soil texture were best predictors of seedling abundance. Species with "poor", "no" and "new" regeneration status necessitate proper attention in forest management plans involving regulation of exotic invasive species populations, grazing and browsing, lopping, fire, over-extraction of non-timber forest produce and prevention of illegal felling through vigilance and more active participation of Forest Protection Committees. [ABSTRACT FROM AUTHOR]

The suspended algal fraction in small lotic waterbodies has rarely been investigated as the ecological importance was questioned due to low abundances. Recent findings suggest, that substantial riverine plankton communities can establish in small lotic waterbodies. The gap of knowledge concerning the composition of suspended algal assemblages and influential factors determining their composition is addressed in this study by monitoring suspended and attached algal communities over a two year period in agriculturally influenced small lotic waterbodies using delayed fluorescence spectroscopy. A quantitative comparison of suspended and attached algae indicated, that suspended algae exceeded attached algal biomass in late summer and early autumn. Redundancy analyses and corresponding generalized linear models indicated that the suspended algal groups were most influenced by nutrients, temperature and velocity, whereas light regime was of minor importance. The attached algal groups were affected by the same factors, however with differing intensity. Regional factors, such as season, were identified as the major gradients along which both algal fractions varied, inducing temporal synchronous algal assemblages. This work represents a contribution towards a better understanding of algal ecology specifically suspended algae in small lotic waterbodies.

Fishery-independent survey sampling programs frequently undergo changes in operational procedures, which have the capacity to alter the catchability coefficient, q . To preserve the continuity of the time series, changes in sampling protocol must be accounted for within the raw data. We used data from a long-standing shark longline survey as a case-study to demonstrate a method of estimating changing catch over variable soak times. Catches of longline sets with and without hook timers were modeled using generalized linear models (GLMs) to estimate catch conversion factors over varying soak times. Estimated conversion factors were used to correct the raw catch data, which were then analyzed with delta-lognormal GLMs to estimate indices of relative abundance. Uncertainty in conversion factor estimation was calculated via bootstrap resampling and propagated through to annual indices by correcting raw data using resampled conversion factors. Added variation introduced by implementation of correction factors was relatively small compared to the magnitude of the observation error of the resulting indices of relative abundance. In species where catch rate declined over soak time, the expected CPUE of shortened soak times increased relative to standard soak times. Contrarily, if catchability increased over soak time, expected CPUE decreased in shortened soak times. Thus, we showed that the predominant practice of treating each unit of sampling effort as equal in fixed, baited gear is not appropriate, and changes in soak time should be accounted for to preserve the longevity of the time series. [ABSTRACT FROM AUTHOR]

Pollination is a key ecosystem service. Pollinators, however, are in decline and their service is increasingly threatened. The decline is driven by several factors, most of which are related to agricultural management. However, the complexity of the landscape system, consisting of both cropped and non-cropped areas, makes it difficult to address or even quantify the role of farming practices in pollinator abundance. Therefore, the aim of this paper is to improve our understanding of the relationships between pollinator abundance and their habitat use. We intend to identify and quantify the driving environmental factors that determine pollinator abundance in agricultural landscapes on the crop and landscape scale. These information helps us to design algorithms that can be used as a basis for predicting pollinator abundance on agricultural fields. To integrate varying environmental conditions data sampling was performed on farms in three different regions in Germany. Pollinators were classified into different groups with three aggregation levels. We observed crop parameters as well as landscape parameters in the areas surrounding fields in addition to temporal aspects. Generalized linear models (GLMs) were then calculated. Our results showed that both crop and landscape parameters affect pollinator abundance on agricultural fields. However, the explanatory power of the included parameters varied strongly among the particular pollinator groups and between aggregation levels. Furthermore, differentiation between species groups improves the explanatory power compared to models that are more aggregated. We also found that the temporal match between main activity periods of the particular pollinator groups and resource supply by the crop species is a key factor when analysing pollinator abundance. In conclusion, we demonstrated that the assessment and support of pollination services should be carried out with regard to individual pollinator groups. When studying pollinator abundance, the crop as well as the landscape scale should be addressed. A range of different habitat requirements and different activity periods of the pollinator groups must be covered to maintain pollination services, and therefore both diverse landscapes and diverse crop rotations are of crucial importance. [ABSTRACT FROM AUTHOR]

Could a disjoint group of enzymes synchronize their activities and execute a complex multi-step, measurable, and reproducible response ? Here, I surmise that the alpha-ketoglutarate dependent superfamily of non-haem iron (II) dioxygenases could influence cell physiology as a cohesive unit, and that the broad spectra of substrates transformed is an absolute necessity to this portrayal.This eclectic group comprises members from all major taxa, and participates in pesticide breakdown, hypoxia signaling, and osmotic stress neutralization. The oxidative decarboxylation of 2-oxoglutarate to succinate is coupled with a concomitant substrate hydroxylation and, in most cases, is followed by an additional specialized conversion. The domain profile of a protein sequence was used as an index of miscellaneous reaction chemistry and combined with available kinetic data to form a linear model of integrated function. Statistical parameters were inferred by the creation of a novel, empirically motivated flat-file database of over 3800 sequences (DB2OG) with putative 2-oxoglutarate dependent activity. The collated information was categorized on the basis of existing annotation schema. The data suggests that the 2OG-dependent superfamily incorporates several desirable features of a systems level player. DB2OG, is free, accessible without a login to all users, and available at the following URL (http://comp-biol.theacms.in/DB2OG.html).

In this article, we adopt the change point approach to monitor the generalized linear profiles in phase II Statistical process control (SPC). Generalized linear profiles include a large class of profiles defined in one framework. In contrast to the conventional change point approach, we adopt the Rao score test rather than the likelihood ratio test. Simulated results show that our approach has a good performance over any possible single step change in process parameters for two special cases of generalized linear profiles, namely Poisson and binomial profiles. Some diagnostic aids are also given and a real example is introduced to shed light on the merits of our approach in real applications. [ABSTRACT FROM AUTHOR]

The suspended algal fraction in small lotic waterbodies has rarely been investigated as the ecological importance was questioned due to low abundances. Recent findings suggest, that substantial riverine plankton communities can establish in small lotic waterbodies. The gap of knowledge concerning the composition of suspended algal assemblages and influential factors determining their composition is addressed in this study by monitoring suspended and attached algal communities over a two year period in agriculturally influenced small lotic waterbodies using delayed fluorescence spectroscopy. A quantitative comparison of suspended and attached algae indicated, that suspended algae exceeded attached algal biomass in late summer and early autumn. Redundancy analyses and corresponding generalized linear models indicated that the suspended algal groups were most influenced by nutrients, temperature and velocity, whereas light regime was of minor importance. The attached algal groups were affected by the same factors, however with differing intensity. Regional factors, such as season, were identified as the major gradients along which both algal fractions varied, inducing temporal synchronous algal assemblages. This work represents a contribution towards a better understanding of algal ecology specifically suspended algae in small lotic waterbodies. [ABSTRACT FROM AUTHOR]

One of the recent advancements in preconstruction safety management is the identification and quantification of risks associated with fundamental attributes of construction work environments that cause injuries. The goal of this paper is to test the validity of using these fundamental risk attributes to predict safety outcomes. The modeling approach required two steps, as follows: (1) a principal component analysis was performed on the safety attributes to reduce dimension of the data and remove collinearity among attributes (the principle component analysis provided insights into the relative importance of the various attributes and provided an orthogonal decomposition of the data), and (2) the leading principal components (which are orthogonal by definition) were used as potential predictors in a generalized linear model with a logit link function to model the probability of different accident categories. The predictive power was then assessed using a rank probability skill score, which quantified the probabilistic skill of the forecasts over the categories. The analysis shows strong predictive skill, making the models attractive for safety managers to use to skilfully forecast the probability of a safety incident given identifiable characteristics of planned work. Researchers in the technology domain may find these models useful in predicting safety outcomes during design, work packaging, and scheduling. [ABSTRACT FROM AUTHOR]

An essential step in understanding the processes underlying the general mechanism of perceptual categorization is to identify which portions of a physical stimulation modulate the behavior of our perceptual system. More specifically, in the context of speech comprehension, it is still a major open challenge to understand which information is used to categorize a speech stimulus as one phoneme or another, the auditory primitives relevant for the categorical perception of speech being still unknown. Here we propose to adapt technique relying on a Generalized Linear Model with smoothness priors technique, already used in the visual domain for estimation of so-called classification images, to auditory experiments. This statistical model offers a rigorous framework for dealing with non-Gaussian noise, as it is often the case in the auditory modality, and limits the amount of noise in the estimated template by enforcing smoother solution. By applying this technique to a specific two-alternative forced choice experiment between stimuli ‘aba’ and ‘ada’ in noise with an adaptive SNR, we confirm that the second formantic transition is a key for classifying phonemes into /b/ or /d/ in noise, and that its estimation by the auditory system is a relative measurement across spectral bands and in relation to the perceived height of the second formant in the preceding syllable. Through this example, we show how the GLM with smoothness priors approach can be applied to the identification of fine functional acoustic cues in speech perception. Finally we discuss some assumptions of the model in the specific case of speech perception.

Summary: With the current concern over climate change, descriptions of how rainfall patterns are changing over time can be useful. Observations of daily rainfall data over the last few decades provide information on these trends. Generalized linear models are typically used to model patterns in the occurrence and intensity of rainfall. These models describe rainfall patterns for an average year but are more limited when describing long-term trends, particularly when these are potentially non-linear. Generalized additive models (GAMs) provide a framework for modelling non-linear relationships by fitting smooth functions to the data. This paper describes how GAMs can extend the flexibility of models to describe seasonal patterns and long-term trends in the occurrence and intensity of daily rainfall using data from Mauritius from 1962 to 2001. Smoothed estimates from the models provide useful graphical descriptions of changing rainfall patterns over the last 40 years at this location. GAMs are particularly helpful when exploring non-linear relationships in the data. Care is needed to ensure the choice of smooth functions is appropriate for the data and modelling objectives. [Copyright &y& Elsevier]

In this paper, we explore some weakly consistent properties of quasi-maximum likelihood estimates (QMLE) concerning the quasi-likelihood equation $$ \sum\nolimits_{i = 1}^n {X_i (y_i - \mu (X_i^\prime \beta ))} $$ for univariate generalized linear model E( y| X) = μ( X′ β). Given uncorrelated residuals { e i = Y i − μ( X β0), 1 ⩽ i ⩽ n} and other conditions, we prove that $$ \hat \beta _n - \beta _0 = O_p (\underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{\lambda } _n^{ - 1/2} ) $$ holds, where $$ \hat \beta _n $$ is a root of the above equation, β 0 is the true value of parameter β and $$ \underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{\lambda } _n $$ denotes the smallest eigenvalue of the matrix S n = ∑ X i X . We also show that the convergence rate above is sharp, provided independent non-asymptotically degenerate residual sequence and other conditions. Moreover, paralleling to the elegant result of Drygas (1976) for classical linear regression models, we point out that the necessary condition guaranteeing the weak consistency of QMLE is S → 0, as the sample size n → ∞. [ABSTRACT FROM AUTHOR]

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Purpose: Identification of the temporal pattern of diarrhea disease in children less than 5 years of age in Rio de Janeiro City (1995–1998) to provide support for decisions about prevention and control of the disease.Methods: The weekly counts of hospitalizations and deaths due to diarrhea disease were analyzed separately. An initial generalized linear model (GLM) was derived using variables related to weather and month. Displays of fitted generalized additive models (GAM) including a spline smoothed function of time suggested additional predictors that were used to obtain new models.Results: The initial models did not properly account for the observed cyclical pattern of the data. Graphical displays of the GAM model show a nonhomogeneous decline and annual cycles. Stepwise fitting of GLMs with two factors (cycle and season), and a time trend, showed that the full three-way interaction model was required. Plots of the residuals from the death model suggested a mixture of distributions while the residuals from the hospitalization model were approximately normal.Conclusions: The same general pattern for both time series was found by graphical inspection and fitting of appropriate GLMs. This study provides some additional evidence that severe cases of diarrhea disease may be attributed to rotavirus. [Copyright &y& Elsevier]

Various factors can lead to the transformation of metal forms and to an increase in their solubility and, consequently, their mobility. One solution to the problem of increased solubility is the use of carbons as additives to soil in order to limit the potential migration of contaminants. The aim of this study was to determine the effect of using brown coal and activated carbon on metal forms that are available to plants. The mineral composition of the coals used in a pot experiment was analyzed. Observations were carried out with a JJSM-6380 LA scanning electron microscope (SEM) connected to an EDS electron micro-probe. The total contents of Zn, Cd, Pb and Cu in the assessed soils as well as the content of available metal forms were determined after single extractions with different reagents, namely 1 M NH4NO3, DTPA and 1 M HCl. Generalized linear models (GLMs) were used to evaluate the effectiveness of the stabilization methods in a long-term pot experiment. The carbons reduced the percentage of these forms relative to the total metal content in the soil. After adding brown coal, Zn, Cd, Pb and Cu forms were reduced by up to 32%, 30%, 33% and 43%, respectively. After adding activated carbon, the metal forms of Zn, Cd, Pb and Cu were reduced by up to 47%, 44%, 40% and 50%, respectively. The following order of extracted metal forms with different solutions was found: HCl: Zn > Pb > Cu > Cd; DTPA: Pb > Zn > Cu > Cd; NH4NO3: Zn > Cu > Pb > Cd. Eight years after setting up the pot experiment, the contents of humic substances in soils with the addition of both tested carbons were compared, and the soils with added carbons were found to have a stable content of humic fractions. The costs of remediation through the stabilization method using the tested brown coal and activated carbons do not exceed USD 75/t (taking into account the double doses of both carbons). [ABSTRACT FROM AUTHOR]

Generalized k -means can be combined with any similarity or dissimilarity measure for clustering. Using the well known likelihood ratio or F -statistic as the dissimilarity measure, a generalized k -means method is proposed to group generalized linear models (GLMs) for exponential family distributions. Given the number of clusters k , the proposed method is established by the uniform most powerful unbiased (UMPU) test statistic for the comparison between GLMs. If k is unknown, then the proposed method can be combined with generalized liformation criterion (GIC) to automatically select the best k for clustering. Both AIC and BIC are investigated as special cases of GIC. Theoretical and simulation results show that the number of clusters can be correctly identified by BIC but not AIC. The proposed method is applied to the state-level daily COVID-19 data in the United States, and it identifies 6 clusters. A further study shows that the models between clusters are significantly different from each other, which confirms the result with 6 clusters., (© 2021 Elsevier B.V. All rights reserved.)

Microdata are required to evaluate the distributive impact of the taxation system as a whole (direct and indirect taxes) on individuals or households. However, in European Union countries this information is usually distributed into two separate surveys: the Household Budget Surveys (HBS), including total household expenditure and its composition, and EU Statistics on Income and Living Conditions (EU-SILC), including detailed information about households' income and direct (but not indirect) taxes paid. We present a parametric statistical matching procedure to merge both surveys. For the first stage of matching, we propose estimating total household expenditure in HBS (Engel curves) using a GLM estimator, instead of the traditionally used OLS method. It is a better alternative, insofar as it can deal with the heteroskedasticity problem of the OLS estimates, while making it unnecessary to retransform the regressors estimated in logarithms. To evaluate these advantages of the GLM estimator, we conducted a computational Monte Carlo simulation. In addition, when an error term is added to the deterministic imputation of expenditure in the EU-SILC, we propose replacing the usual Normal distribution of the error with a Chi-square type, which allows a better approximation to the original expenditures variance in the HBS. An empirical analysis is provided using Spanish surveys for years 2012-2016. In addition, we extend the empirical analysis to the rest of the European Union countries, using the surveys provided by Eurostat (EU-SILC, 2011; HBS, 2010)., Competing Interests: No potential conflict of interest was reported by the author(s)., (© 2020 Informa UK Limited, trading as Taylor & Francis Group.)

Important decisions in the heat of battle occur rapidly and a key aptitude of a good combat soldier is the ability to determine whether he is under fire. This rapid decision requires the soldier to make a judgment in a fraction of a second, based on a barrage of multisensory cues coming from multiple modalities. The present study uses an oddball paradigm to examine listener ability to differentiate shooter locations from audio recordings of small arms fire. More importantly, we address the neural correlates involved in this rapid decision process by employing single-trial analysis of electroencephalography (EEG). In particular, we examine small arms expert listeners as they differentiate the sounds of small arms firing events recorded at different observer positions relative to a shooter. Using signal detection theory, we find clear neural signatures related to shooter firing angle by identifying the times of neural discrimination on a trial-to-trial basis. Similar to previous results in oddball experiments, we find common windows relative to the response and the stimulus when neural activity discriminates between target stimuli (forward fire: observer 0° to firing angle) vs. standards (off-axis fire: observer 90° to firing angle). We also find, using windows of maximum discrimination, that auditory target vs. standard discrimination yields neural sources in Brodmann Area 19 (BA 19), i.e., in the visual cortex. In summary, we show that single-trial analysis of EEG yields informative scalp distributions and source current localization of discriminating activity when the small arms experts discriminate between forward and off-axis fire observer positions. Furthermore, this perceptual decision implicates brain regions involved in visual processing, even though the task is purely auditory. Finally, we utilize these techniques to quantify the level of expertise in these subjects for the chosen task, having implications for human performance monitoring in combat.