Search Results (13)

The continuous and effective monitoring of the water quality of small rural rivers is crucial for rural sustainable development. In this work, machine learning models were established to predict the water quality of a typical small rural river based on a small quantity of measured water quality data and UAV hyperspectral images. Firstly, the spectral data were preprocessed using fractional order derivation (FOD), standard normal variate (SNV), and normalization (Norm) to enhance the spectral response characteristics of the water quality parameters. Second, a method combining the Pearson’s correlation coefficient and the variance inflation factor (PCC–VIF) was utilized to decrease the dimensionality of features and improve the quality of the input data. Again, based on the screened features, a back-propagation neural network (BPNN) model optimized using a mixture of the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm was established as a means of estimating water quality parameter concentrations. To intuitively evaluate the performance of the hybrid optimization algorithm, its prediction accuracy is compared with that of conventional machine learning algorithms (Random Forest, CatBoost, XGBoost, BPNN, GA–BPNN and PSO–BPNN). The results show that the GA–PSO–BPNN model for turbidity (TUB), ammonia nitrogen (NH₃-N), total nitrogen (TN), and total phosphorus (TP) prediction exhibited optimal accuracy with coefficients of determination (R²) of 0.770, 0.804, 0.754, and 0.808, respectively. Meanwhile, the model also demonstrated good robustness and generalization ability for data from different periods. In addition, we used this method to visualize the water quality parameters in the study area. This work provides a new approach to the refined monitoring of water quality in small rural rivers. Full article

In the present paper, based on the representation of the logistic distribution as a normal scale mixture obtained by L. Stefanski in 1990, it is demonstrated that the logistic distribution can be limiting for sums of a random number of random variables and other statistics that admit (at least asymptotically) additive representation and are constructed from samples with random sizes. These results complement a theorem proved by B. V. and D. B. Gnedenko in 1982 that established convergence of the distributions of extreme order statistics in samples with geometrically distributed random sizes to the logistic distribution. Hence, along with the normal law, this distribution can be used as an asymptotic approximation of the distributions of observations that can be assumed to have an additive structure, for example, random-walk-type time series. An approach is presented for the definition of the new asymmetric generalization of the logistic distribution as a special normal variance–mean mixture. Full article

This paper is set to analytically describe properties of the hyperbolic distribution. This law, along with the variance-gamma distribution, is one of the most popular normal mean–variance mixtures from the point of view of various applications. We have found closed form expressions for the cumulative distribution and partial-moment-generating functions of the hyperbolic distribution. The obtained formulas use the values of the Humbert confluent hypergeometric and Whittaker special functions. The results are applied to the problem of European option pricing in the related Lévy model of financial market. The research demonstrates that the discussed normal mean–variance mixture is analytically tractable. Full article

Learning distance metrics and distinguishing between samples from different classes are among the most important topics in machine learning. This article proposes a new distance metric learning approach tailored for highly imbalanced datasets. Imbalanced datasets suffer from a lack of data in the minority class, and the differences in class density strongly affect the efficiency of the classification algorithms. Therefore, the density of the classes is considered the main basis of learning the new distance metric. It is possible that the data of one class are composed of several densities, that is, the class is a combination of several normal distributions with different means and variances. In this paper, considering that classes may be multimodal, the distribution of each class is assumed in the form of a mixture of multivariate Gaussian densities. A density-based clustering algorithm is used for determining the number of components followed by the estimation of the parameters of the Gaussian components using maximum a posteriori density estimation. Then, the Bhattacharya distance between the Gaussian mixtures of the classes is maximized using an iterative scheme. To reach a large between-class margin, the distance between the external components is increased while decreasing the distance between the internal components. The proposed method is evaluated on 15 imbalanced datasets using the k-nearest neighbor (KNN) classifier. The results of the experiments show that using the proposed method significantly improves the efficiency of the classifier in imbalance classification problems. Also, when the imbalance ratio is very high and it is not possible to correctly identify minority class samples, the proposed method still provides acceptable performance. Full article

Background: Fold change is a common metric in biomedical research for quantifying group differences in omics variables. However, inconsistent calculation methods and inadequate reporting lead to discrepancies in results. This study evaluated various fold-change calculation methods aiming at a recommendation of a preferred approach. Methods: The primary distinction in fold-change calculations lies in defining group expected values for log ratio computation. To challenge method interchangeability in a “stress test” scenario, we generated diverse artificial data sets with varying distributions (identity, uniform, normal, log-normal, and a mixture of these) and compared calculated fold-changes to known values. Additionally, we analyzed a multi-omics biomedical data set to estimate to what extent the findings apply to real-world data. Results: Using arithmetic means as expected values for treatment and reference groups yielded inaccurate fold-change values more frequently than other methods, particularly when subgroup distributions and/or standard deviations differed significantly. Conclusions: The arithmetic mean method, often perceived as standard or picked without considering alternatives, is inferior to other definitions of the group expected value. Methods using median, geometric mean, or paired fold-change combinations are more robust against violations of equal variances or dissimilar group distributions. Adhering to methods less sensitive to data distribution without trade-offs and accurately reporting calculation methods in scientific reports is a reasonable practice to ensure correct interpretation and reproducibility. Full article

Cement-stabilized rammed earth (CSRE) is a variation of the traditional rammed earth building material, which has been used since ancient times, strengthened by the addition of a stabilizer in the form of Portland cement. This article compares the compressive strength of CSRE determined from specimens cored from structural walls and those molded in the laboratory. Both types of specimens underwent a 120-day curing period. The tests were conducted on specimens with various grain sizes and cement content. An analysis of variance (ANOVA) was performed on the obtained results to determine whether it is possible to establish a conversion factor between the compressive strength values obtained from laboratory-molded cubic samples and those from cored samples extracted from the CSRE structure. The study revealed that the compressive strength of CSRE increases significantly over the curing period, with substantial strength gains observed up to 120 days. The results indicated no statistically significant difference in the mean unconfined compressive strength (UCS) between cubic and cored specimens for certain mixtures, suggesting that a shape coefficient factor may not be necessary for calculating CSRE compressive strength in laboratory settings. However, for other mixtures, normal distribution was not confirmed. These findings have implications for the standardization and practical application of CSRE in construction, highlighting the need for longer curing periods to achieve optimal strength and the potential to simplify testing protocols. Full article

This paper presents a voice biometrics system implemented in a web application as part of a two-factor authentication (2FA) user login. The web-based application, via a client interface, runs registration, preprocessing, feature extraction and normalization, classification, and speaker verification procedures based on a modified Gaussian mixture model (GMM) algorithm adapted to the application requirements. The article describes in detail the internal modules of this ASR (Automatic Speaker Recognition) system. A comparison of the performance of competing ASR systems using the commercial NIST 2002 SRE voice dataset tested under the same conditions is also presented. In addition, it presents the results of the influence of the application of cepstral mean and variance normalization over a sliding window (WCMVN) and its relevance, especially for voice recordings recorded in varying acoustic tracks. The article also presents the results of the selection of a reference model representing an alternative hypothesis in the decision-making system, which significantly translates into an increase in the effectiveness of speaker verification. The final experiment presented is a test of the performance achieved in a varying acoustic environment during remote voice login to a web portal by the test group, as well as a final adjustment of the decision-making threshold. Full article

Every year more than 150,000 tons of resin used in a myriad of industrial applications are produced by Brazilian plantations of Pinus elliottii Engelm. (slash pine), which are also used for timber. A pine tree can be tapped for resin over a period of several years. Resin is a complex mixture of terpenes, which are carbon-rich molecules, presumably influencing pine plantation carbon budgets. A total of 270 trees (overall mean DBH of 22.93 ± 0.11 cm) of 14-, 24-, and 26-year-old stands had their C content measured. Three different treatments (intact, wounded panels, and wounded + chemically stimulated panels, 30 trees each) were applied per site. Above- and belowground biomass, as well as resin yield, were quantified for two consecutive years. Data were statistically evaluated using normality distribution tests, analyses of variance, and mean comparison tests (p ≤ 0.05). The highest resin production per tree was recorded in the chemically stimulated 14-year-old stand. Tree dry wood biomass, a major stock of carbon retained in cell wall polysaccharides, ranged from 245.69 ± 11.73 to 349.99 ± 16.73 kg among the plantations. Variations in carbon concentration ranged from 43% to 50% with the lowest percentages in underground biomass. There was no significant difference in lignin concentrations. Soils were acidic (pH 4.3 ± 0.10–5.83 ± 0.06) with low C (from 0.05% to 1.4%). Significantly higher C stock values were recorded in pine biomass compared to those reported for temperate zones. Resin-tapping biomass yielded considerable annual increments in C stocks and should be included as a relevant component in C sequestration assessments of planted pine forests. Full article

A change point is a location or time at which observations or data obey two different models: before and after. In real problems, we may know some prior information about the location of the change point, say at the right or left tail of the sequence. How does one incorporate the prior information into the current cumulative sum (CUSUM) statistics? We propose a new class of weighted CUSUM statistics with three different types of quadratic weights accounting for different prior positions of the change points. One interpretation of the weights is the mean duration in a random walk. Under the normal model with known variance, the exact distributions of these statistics are explicitly expressed in terms of eigenvalues. Theoretical results about the explicit difference of the distributions are valuable. The expansions of asymptotic distributions are compared with the expansion of the limit distributions of the Cramér-von Mises statistic and the Anderson and Darling statistic. We provide some extensions from independent normal responses to more interesting models, such as graphical models, the mixture of normals, Poisson, and weakly dependent models. Simulations suggest that the proposed test statistics have better power than the graph-based statistics. We illustrate their application to a detection problem with video data. Full article

The Behrens–Fisher problem occurs when testing the equality of means of two normal distributions without the assumption that the two variances are equal. This paper presents approaches based on the exact and near-exact distributions for the test statistic of the Behrens–Fisher problem, depending on different combinations of even or odd sample sizes. We present the exact distribution when both sample sizes are odd and the near-exact distribution when one or both sample sizes are even. The near-exact distributions are based on a finite mixture of generalized integer gamma (GIG) distributions, used as an approximation to the exact distribution, which consists of an infinite series. The proposed tests, based on the exact and the near-exact distributions, are compared with Welch’s t-test through Monte Carlo simulations, in particular for small and unbalanced sample sizes. The results show that the proposed approaches are competent solutions to the Behrens–Fisher problem, exhibiting precise sizes and better powers than Welch’s approach for those cases. Numerical studies show that the Welch’s t-test tends to be a bit more conservative than the test statistics based on the exact or near-exact distribution, in particular when sample sizes are small and unbalanced, situations in which the proposed exact or near-exact distributions obtain higher powers than Welch’s t-test. Full article

This paper introduces a new methodology to estimate time-varying alphas and betas in conditional factor models, which allows substantial flexibility in a time-varying framework. To circumvent problems associated with the previous approaches, we introduce a Bayesian time-varying parameter model where innovations of the state equation have a spike-and-slab mixture distribution. The mixture distribution specifies two states with a specific probability. In the first state, the innovation variance is set close to zero with a certain probability and parameters stay relatively constant. In the second state, the innovation variance is large and the change in parameters is normally distributed with mean zero and a given variance. The latent state is specified with a threshold that governs the state change. We allow a separate threshold for each parameter; thus, the parameters may shift in an unsynchronized manner such that the model moves from one state to another when the change in the parameter exceeds the threshold and vice versa. This approach offers great flexibility and nests a plethora of other time-varying model specifications, allowing us to assess whether the betas of conditional factor models evolve gradually over time or display infrequent, but large, shifts. We apply the proposed methodology to industry portfolios within a five-factor model setting and show that the threshold Capital Asset Pricing Model (CAPM) provides robust beta estimates coupled with smaller pricing errors compared to the alternative approaches. The results have significant implications for the implementation of smart beta strategies that rely heavily on the accuracy and stability of factor betas and yields. Full article

The aim of this study is to develop climate-sensitive single-tree growth models, to be used in stand based prediction systems of managed forest in Switzerland. Long-term observations from experimental forest management trials were used, together with retrospective climate information from 1904 up to 2012. A special focus is given to the role of transformation of modelling basal area increment, helping to normalize the random error distribution. A nonlinear model formulation was used to describe the basic relation between basal area increment and diameter at breast height. This formulation was widely expanded by groups of explanatory variables, describing competition, stand development, site, stand density, thinning, mixture, and climate. The models are species-specific and contain different explanatory variables per group, being able to explain a high amount of variance (on the original scale, up to 80% in the case of Quercus spec.). Different transformations of the nonlinear relation where tested and based on the mean squared error, the square root transformation performed best. Although the residuals were homoscedastic, they were still long-tailed and not normal distributed, making robust statistics the preferred method for statistical inference. Climate is included as a nonlinear and interacting effect of temperature, precipitation and moisture, with a biological meaningful interpretation per tree species, e.g., showing better growth for Abies alba in warm and wet climates and good growing conditions for Picea abies in colder and dryer climates, being less sensitive on temperature. Furthermore, a linear increase in growth was found to be present since the 1940s. Potentially this is an effect of the increased atmospheric CO₂ concentration or changed management in terms of reduced nutrient subtractions from forest ground, since industrialization lowered the demand of residue and slash uptake. Full article

We consider a one-period portfolio optimization problem under model uncertainty. For this purpose, we introduce a measure of model risk. We derive analytical results for this measure of model risk in the mean-variance problem assuming we have observations drawn from a normal variance mixture model. This model allows for heavy tails, tail dependence and leptokurtosis of marginals. The results show that mean-variance optimization is seriously compromised by model uncertainty, in particular, for non-Gaussian data and small sample sizes. To mitigate these shortcomings, we propose a method to adjust the sample covariance matrix in order to reduce model risk. Full article