Geomechnical model testing has been widely applied as a kind of research technique in underground engineering problems. However, during the practical application process, due to the influence of many factors, the desired results cannot be obtained. In order to solve this problem, based on the measurement requirements of the model test, combined with FBG(Fiber Bragg Grating) sensor technology and traditional measurement methods, an FBG monitoring system, Micro-multi-point displacement test system, resistance strain test system and surrounding rock pressure monitoring system are developed. Applying the systems to a model test of the tunnel construction process, the displacement in advance laws of tunnel face, radial displacement distribution laws and surrounding rock pressure laws are obtained. Test results show that a multivariate information monitoring system has the advantage of high precision, stability and strong anti-jamming capability. It lays a solid foundation for the real-time data monitoring of the tunnel construction process model test.
Due to inadequate efforts to reinforce nitrogen fixation capability of bean via symbiosis with rhizobia, improvement of bean productivity is still highly dependent on chemical fertilization. An advanced understanding of agro-ecosystem-bean-Rhizobium interaction is required to improve symbiosis efficiency. Thus, seasonal development of rhizobial nodulation was characterized according to 20 agro-ecological properties for 122 commercial bean fields. Principal component analysis identified soil texture as a major descriptor of agrosystem-bean-disease-Rhizobium interaction. Nonparametric correlation analysis indicated significant associations of root nodulation with bean class, fungicidal treatment of seed and soil, Fusarium root rot index, planting date and depth, soil texture, clay and sand content. Ordinal regression analysis demonstrated that rhizobial nodulation was improved by applying initial drought, heavier soil textures with greater organic matter and neutral pH, using herbicides and manure, growing white beans, irrigating every 7–9 days, later sowing in June, reducing disease and weed, shallower seeding, sowing beans after alfalfa, avoiding fungicidal treatment of seed and soil, and omitting urea application. This largescale study provided novel information on a comprehensive number of agronomic practices as potential tools for improving bean-Rhizobium symbiosis for sustainable legume production systems.
The aim of this paper is to examine the empirical usefulness of two new MSF – Scalar BEKK(1,1) models of n-variate volatility. These models formally belong to the MSV class, but in fact are some hybrids of the simplest MGARCH and MSV specifications. Such hybrid structures have been proposed as feasible (yet non-trivial) tools for analyzing highly dimensional financial data (large n). This research shows Bayesian model comparison for two data sets with n = 2, since in bivariate cases we can obtain Bayes factors against many (even unparsimonious) MGARCH and MSV specifications. Also, for bivariate data, approximate posterior results (based on preliminary estimates of nuisance matrix parameters) are compared to the exact ones in both MSF-SBEKK models. Finally, approximate results are obtained for a large set of returns on equities (n = 34).
The s-period ahead Value-at-Risk (VaR) for a portfolio of dimension n is considered and its Bayesian analysis is discussed. The VaR assessment can be based either on the n-variate predictive distribution of future returns on individual assets, or on the univariate Bayesian model for the portfolio value (or the return on portfolio). In both cases Bayesian VaR takes into account parameter uncertainty and non-linear relationship between ordinary and logarithmic returns. In the case of a large portfolio, the applicability of the n-variate approach to Bayesian VaR depends on the form of the statistical model for asset prices. We use the n-variate type I MSF-SBEKK(1,1) volatility model proposed specially to cope with large n. We compare empirical results obtained using this multivariate approach and the much simpler univariate approach based on modelling volatility of the value of a given portfolio.
The aim of this study was to examine the changes in the chemical composition of shallow groundwater and its quality that have occurred in the last decade in an agriculturally used, heavily populated and characterized by a complex geological structure, catchment of the Stara Rzeka river, located in the flysch part of the Outer Carpathians. Water samples were collected during 2013 from 19 still operating wells. Analyses of pH, electrolytic conductivity and chemical composition by ion chromatography were conducted. The obtained results were compared with the results of studies conducted in 2003 for the same wells. The quality of groundwater and its suitability for consumption was assessed based on the regulations currently existing in Poland. 21% of the wells still do not meet the requirements for drinking water in terms of at least one component. However, there was a decrease in the concentration of mineral forms of nitrogen and phosphorus in most of the wells and their mean concentration as compared to 2003 was reduced. In terms of physical and chemical characteristics groundwater of this region is typical of the hypergenic zone of the temperate climate. The highest concentrations were observed for Ca2+ and HCO3- ions, while K+ and Cl- were characterized by the largest variability. Principal Component Analysis (PCA) demonstrated that the factors determining the quality and chemical composition of the analyzed waters include the composition of bedrock (mineralogy of the rock environment) and human economic activity, and that they have not been significantly changed over the past decade.
The aim of this study was the application of the geo-accumulation index and geostatistical methods to the assessment of forest soil contamination with heavy metals in the Babia Góra National Park (BGNP). For the study, 59 sample plots were selected to reflect all soil units (soil subtypes) in the studied area and take into account various forms of terrain. The content of organic carbon and total nitrogen, pH, hydrolytic acidity, the base cations and heavy metals content were determined in the soil samples. The geo-accumulation index (Igeo) was calculated, enabling estimation of the degree of soil pollution. The tested soils are characterized by strong contamination with heavy metals, especially with lead. The concentration of heavy metals in the surface horizons of the tested soils exceeds allowable concentration. The content of heavy metals was related to the content of soil organic matter, soil acidity and altitude. Higher altitudes are dominated by coniferous tree stands, which are accompanied by acidic, poorly decomposed organic horizons. Our study has confirmed the impact of pollutants transported from industrial areas on the amount of heavy metals in soils of the BGNP.
The pollen morphology of many collections of taxa of the tribe Nigelleae from the family Ranunculaceae which occur worldwide is presented in this study. A total of 88 specimens from 21 taxa, some of which were recently proposed, belonging to the genera Komaroffia, Garidella, and Nigella of Nigelleae were examined using light microscopy (LM) and scanning electron microscopy (SEM). In the tribe, the pollen type is mostly trizonocolpate, but in many taxa and specimens, both trizonocolpate and non-trizonocolpate types occur together. The pollen grains are small to medium (25–53.75 μm × 20–55 μm) in size and oblate to prolate in shape. The exine pattern at the mesocolpium in all the taxa investigated is similar: micro-echinate in LM and micro-echinate-punctate in SEM. The colpus membrane in Komaroffia and Nigella is micro-echinate in both LM and SEM. In Garidella, it is micro-echinate in LM but echinate (spinulose) in SEM. In this study, multivariate analyses, principal component analysis (PCA), and unweighted pair group method with arithmetic mean (UPGMA), were used to evaluate relationships between the genera and species within the tribe with respect to pollen morphology. PCA results show three main groups in the tribe: Garidella, Komaroffia, and Nigella. Moreover, the UPGMA tree also chiefly supports generic segregation into the smaller genera. An overall synthesis of the pollen characteristics of the three genera is provided and discussed.
Portulaca oleracea L. (Portulacaceae) is used as functional food and its nutritional and therapeutic properties are related to the high levels of organic and fatty acids, polyphenols, polysaccharides and cyclo-dopa amides. This study presents a strategy based on liquid chromatography – high resolution accurate mass spectrometry method (LC – HRAMS) and bioinformatic methods to analyze 33 purslane accessions originating from 11 floristic regions in Bulgaria together with 5 accessions of Greek provenance. Extracts were obtained by microwave extraction. Based on the LC-MS metabolic “fingerprints” of assayed samples, a purslane metabolic database was developed. LC-MS data were proceeded with Software application Compound Discover 2.0 (Thermo Fischer Sci., USA). Principal Component Analysis (PCA) combined with both descriptive and differential analyses were used to find marker metabolites to distinguish different geographical regions. The differential analysis of the Bulgarian and Greek samples allowed the identification of 50 marker metabolites. Based on accurate masses, retention times, fragmentation patterns in MS/MS, comparison with commercial standards and literature data, these secondary metabolites were identified after detailed analysis of Volcano-plots. For the first time, 29 compounds are reported. The identified compounds were used to perform a study of the biosynthetic pathways of purslane secondary metabolites using Kyoto Encyclopedia of Genes and Genomes (KEGG) software platform. The statistical treatments identified marker compounds that can be used to distinguish the origin of accession set. Combining LC-MS data with multivariate statistical analysis was shown to be effective in studying the purslane metabolites, allowing for integration of chemistry with geographic origin.
The first so-called hybrid MSV-MGARCH models were characterized by the conditional covariance matrix that was a product of a univariate latent process and a matrix with a simple MGARCH structure (Engle’s DCC or scalar BEKK). The aim was to parsimoniously describe volatility of a large group of assets. The proposed hybrid models, similarly as pure MSV specifications (and other models based on latent processes), required the Bayesian approach equipped with efficient MCMC simulation tools. The numerical effort has payed – the hybrid models seem particularly useful due to their good fit and ability to jointly cope with large portfolios. In particular, the simplest hybrid, now called the MSF-SBEKK model, has been successfully used in many applications. However, one latent process may be insufficient in the case of a highly heterogeneous portfolio. Thus, in this study we discuss a general hybrid MSV-MGARCH model structure, showing its basic characteristics that explain greater flexibility of such hybrid structure with respect to the corresponding MGARCH class. From the empirical perspective, we advocate the GMSF-SBEKK specification, which uses as many latent processes as there are relatively homogeneous groups of assets. We present full Bayesian inference for such models, with the use of an efficient MCMC simulation strategy. The approach is used to jointly model volatility on very different markets. Joint modelling is formally compared to individual modelling of volatility on each market.
We study the autocovariance structure of a general Markov switching second-order stationary VARMA model.Then we give stable finite order VARMA(p*, q*) representations for those M-state Markov switching VARMA(p, q) processes where the observables are uncorrelated with the regime variables. This allows us to obtain sharper bounds for p* and q* with respect to the ones existing in literature. Our results provide new insights into stochastic properties and facilitate statistical inference about the orders of MS-VARMA models and the underlying number of hidden states.
The aim of the study is to formally compare the explanatory power of Copula-GARCH and MGARCH models. The models are estimated for logarithmic daily rates of return of two exchange rates: EUR/PLN, USD/PLN and stock market indices: SP500, BUX. The analysis is performed within the Bayesian framework. The posterior model probabilities point to AR(1)-tSBEKK(1,1) for the exchange rates and VAR(1)-tCopula-GARCH(1,1) for the stock market indices, as the superior specifications. If the marginal sampling distributions are different in terms of tail thickness, the Copula-GARCH models have higher explanatory power than the MGARCH models.
In this paper, an attempt was made to find out two empirical relationships incorporating linear multivariate regression (LMR) and gene expression programming (GEP) for predicting the blast-induced ground vibration (BIGV) at the Sarcheshmeh copper mine in south of Iran. For this purpose, five types of effective parameters in the blasting operation including the distance from the blasting block, the burden, the spacing, the specific charge, and the charge per delay were considered as the input data while the output parameter was the BIGV. The correlation coefficient and root mean squared error for the LMR were 0.70 and 3.18 respectively, while the values for the GEP were 0.91 and 2.67 respectively. Also, for evaluating the validation of these two methods, a feed-forward artificial neural network (ANN) with a 5-20-1 structure has been used for predicting the BIGV. Comparisons of these parameters revealed that both methods successfully suggested two empirical relationships for predicting the BIGV in the case study. However, the GEP was found to be more reliable and more reasonable.
Popular statistical techniques, such as Spearman's rank correlation matrix, principal component analysis (PCA) and multiple linear regression analysis were applied to analyze a large set of water quality data of the Rybnik Reservoir generated during semiannual monitoring. Water samples collected at 9 sampling sites located along the main axis of the reservoir were tested for 14 selected parameters: concentrations of co-occurring elements, ions and physicochemical parameters. The aim of this study was to estimate the impact of those parameters on inorganic arsenic occurrence in Rybnik Reservoir water by means of multivariate statistical methods. The spatial distribution of arsenic in Rybnik Power Station reservoir was also included. Inorganic arsenic As(III), As(V) concentrations were determined by hydride generation method (HG-AAS) using SpectrAA 880 spectrophotometer (Varian) coupled with a VGA-77 system for hydride generation and ECT-60 electrothermal furnace. Spearman's rank correlation matrix was used in order to find existing correlations between total inorganic arsenic (AsTot) and other parameters. The results of this analysis suggest that As was positively correlated with PO43-; Fe and TDS. PCA confirmed these observations. Principal component analysis resulted in three PC's explaining 57% of the total variance. Loading values for each component indicate that the processes responsible for As release and distribution in Rybnik Reservoir water were: leaching from bottom sediments together with other elements like Cu, Cd, Cr, Pb, Zn, Ni, Ca (PC1) and co-precipitation with PO43-, Fe and Mn (PC3) regulated by physicochemical properties like T and pH (PC2). Finally, multiple linear regression model has been developed. This model incorporates only 8 (T, pH, PO43-, Fe, Mn, Cr, Cu, TDS) out of initial 14 variables, as the independent predictors of total As contamination level. This study illustrates the usefulness of multivariate statistical techniques for analysis and interpretation of complex environmental data sets.
Anestrus is essential to an unsuccessful pregnancy in dairy cows. One of the many factors that influences anestrus is the inactive ovary. To characterize in detail the plasma metabolic pro- file, anestrus cows suffering from inactive ovaries were compared with those with natural estrus. The Holstein cows 60 to 90 day postpartum in an intensive dairy farm were assigned into inactive ovaries groups (IO, n=20) and natural estrus group (CON, n=22) according to estrus signs and rectal palpation of ovaries. Plasma samples from two groups of cows were collected from the tail vein to screen differential metabolites using gas chromatography/mass spectrometry (GC/MS) techniques and multivariate statistical analysis and pathways. The results showed that 106 compounds were screened by GC/MS and 14 compounds in the IO group were decreased by analyzing important variables in the projection values and p values of MSA.Through pathway analysis, 14 compounds, mainly associated with carbohydrate metabolism and amino acid meta- bolism, were identified to results in IO, which may seriously affect follicular growth. Metabolo- mics profiling, together with MSA and pathway analysis, showed that follicular growth and development in dairy cows is related to carbohydrate and amino acid metabolism by a single or multiple pathway(s).
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