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Hydrochemical studies, involving groundwater analysis, multivariate statistics, and geospatial element distributions, were carried out to understand the factors influencing groundwater geochemistry and potential pollution in an arid area (Eastern Desert, Egypt). A total of 104 groundwater samples were analyzed for twenty-three physical and chemical variables. Spatial mapping was conducted to depict variations in the concentration of several variables. A spatial pattern revealed an increasing water table depth from south to north, correlating with a rise in groundwater salinity. The correlation coefficients for various variables were found to align with aquifer mineral saturation indices. Hydrochemical facies identified three main zones with six facies in the first sub-area, five in the second, and the third sub-areas. Specific processes, including evaporite/carbonate rock dissolution and ion exchange, dominate. The results support meteoric groundwater recharge in the first sub-area, influenced by the Nile water and rainfall. The second and third sub-areas indicated groundwater mixing from diverse sources, emphasizing evaporite/carbonate rock dissolution and ion exchange. Negative saturation indices for anhydrite, halite, and gypsum suggested under-saturation conditions, while dolomite and calcite indicated upper saturation, implying secondary mineral precipitation. Factor analysis demonstrated that six interrelated factors elucidate the chemical attributes of the groundwater. These factors arise from interactions between rock and water, blending of waters from diverse sources, and, to some extent, anthropogenic influences. Utilizing cluster analysis, notable and distinct groundwater zones were identified, where the original groundwater was uniquely influenced by mixing processes. Therefore, integration of hydrochemical and geospatial statistical analysis methods can be employed to enhance water resource management on a regional scale and in areas with similar conditions. [ABSTRACT FROM AUTHOR]

An appraisal of the snail (Mollusca: Gastropoda) assemblage of the Sundarbans mangrove region, West Bengal, India, was made through consistent monitoring of three selected sites. In a span of 3 years, 18 species of snails under 11 families, and 9 orders were collected in 304 samples with the Shanon-Weiner diversity index (H') ranging between 0.8445 and 1.6909. In terms of numerical abundance, Stenothyra deltae (34.07 ± 5.71SE) and Cerithidia cingulata (26.01 ± 1.33SE) were dominant, while Nassarius stolatus (0.01 ± 0.01 SE) qualified as a rare species in the three sites. Significant differences (p < 0.05) in abundance in the sites were observed for Cerithidia cingulata, C. alata, Gangetica miliacea, and Stenothyra deltae. A canonical correspondence analysis reflected that the abundance of G. miliacea is shaped by the conductivity, C. cingulata is linked with temperature and the phosphate concentration is a better predictor of the abundance of S. deltae. The pattern of the diversity of the snails may probably reflect the variations in the environmental quality including the food resources. Thus, the snail assemblage pattern may be used as a basis for the biological monitoring of the Sundarbans mangroves and other similar sites. [ABSTRACT FROM AUTHOR]

Some of the collected data by wireless sensor networks (WSNs) often digress from the normal pattern of the set and are called the 'outliers' or 'anomalies'. These outlier data are usually derived from an error that can be due to the constraints of sensor nodes such as problems in the processing unit, power supply, and component failure or an event occurring at the development site of sensor nodes, including the earthquake or flood, affecting the quality of the collected data and their reliability for decision making. In this work, a method is proposed for detecting outlier data in networks based on the hidden Markov model and the copula theory. Copula was used because of its ability to deal with multivariate sets and its unlimitedness in such a way that it required no assumption about data distribution, and Markov has been applied due to its good performance in forecasting. The results of evaluations on the actual data from the Intel laboratory represent the efficiency of the proposed method. Bases on the performance evaluations of the proposed method, in comparison with a relatively new work leading to good results. [ABSTRACT FROM AUTHOR]

Accurate measurement of meteorological parameters is crucial for weather forecasting and climate change research. However, missing observations in rainfall data can pose a challenge to these efforts. Traditional methods of imputation can lead to increased uncertainty in predictions. Additionally, varying lengths of missing data and nonlinearity in rainfall distribution make it difficult to rely on a single imputation method in all situations. To address this issue, our study compared univariate and multivariate imputation models for different lengths of missing daily rainfall observations in a humid tropical region. We used 33 years of weather data from Kozhikode, an urban city in Kerala region, and evaluated the selected models using accuracy measures such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Nash–Sutcliffe Efficiency (NSE) and Mean Absolute Relative Error (MARE). Among the considered univariate and multivariate imputation models, Kalman filter coupled time series models like Kalman–Arima ( RMSE ¯ = 11.90, MAE ¯ = 4.46) and Kalman Smoothing with structure time series ( RMSE ¯ = 11.37, MAE ¯ = 5.28) were found to be best for small (< 7 days) range imputation of rainfall data. Random Forest ( RMSE ¯ = 16.57, MAE ¯ = 8.0) and Kalman Smoothing with structure time series ( RMSE ¯ = 16.84, MAE ¯ = 8.09) performed well for medium range (8–15 days) of rainfall imputation. Random Forest technique was found to be suitable for large (≤ 30 days) ( RMSE ¯ = 15.45, MAE ¯ = 6.77), and very large (> 30 days) ( RMSE ¯ = 12.91, MAE ¯ = 3.42) missing length groups and Kalman–ARIMA performed best for mixed day series (RMSE = 9.7, MAE = 3.52). NSE and MARE values for different gap margins in rainfall data (≥ 1 mm) suggest that Kalman Smoothing (KS) connected models, as a representative univariate model, perform exceptionally well when dealing with a small number of missing observations. Notably, multivariate models like Principal Component Analysis (PCA) and Random Forest outperformed univariate models for medium to large gap margins. Considering these findings, utilizing multivariate techniques is recommended for imputing a large number of missing rainfall values and univariate models can be limited for small range of rainfall missing data imputation. The identified imputation models provide effective solutions for filling missing data of various lengths in all stations' datasets in humid tropical regions, thus enhancing rainfall-related analysis and enabling more accurate weather forecasts and climate change research. [ABSTRACT FROM AUTHOR]

Data analysis methods for the study of treatments or exposures in relation to a clinical outcome in the presence of competing risks have a long history, often with inference targets that are hypothetical, thereby requiring strong assumptions for identifiability with available data. Here data analysis methods are considered that are based on single and higher dimensional marginal hazard rates, quantities that are identifiable under standard independent censoring assumptions. These lead naturally to joint survival function estimators for outcomes of interest, including competing risk outcomes, and provide the basis for addressing a variety of data analysis questions. These methods will be illustrated using simulations and Women's Health Initiative cohort and clinical trial data sets, and additional research needs will be described. [ABSTRACT FROM AUTHOR]

In this study, Enzyme-Induced Carbonate Precipitation (EICP) combined with polypropylene fiber technology was used to solidify desert sand, the orthogonal theory was used to design the different levels of the five test variables of cementation solution concentration, urease concentration, enzyme gel ratio, fiber length and fiber content, and the curing effect of EICP combined with polypropylene fiber was studied by macroscopic test of unconfined compressive strength and calcium carbonate content and microscopic test of scanning electron microscope (SEM) and nuclear magnetic resonance (NMR). The results show that the primary and secondary order of the influence of the five test variables on the unconfined compressive strength and calcium carbonate content is: fiber content >fiber length >urease concentration >enzyme gel ratio >cementation solution concentration, in which the fiber length and fiber content had a significant effect on it, and the urease concentration had a little significant effect on it. The incorporation of polypropylene fiber promotes the deposition of calcium carbonate, restricts the displacement and deformation of sand particles, effectively fills the pores between particles, significantly reduces the pore size, and improves the pore structure, which provides a reference for subsequent research and practical geotechnical applications. [ABSTRACT FROM AUTHOR]

Buffalo bull semen traits are economically important traits that influence farm fertility and profitability. Genetic improvement of semen characteristics is an important detail of the genetic improvement. This study was conducted to assess the relationship between the breeding values as well as the phenotypic values for semen traits (VOL, MM, LS, AS and CONC) of the Egyptian buffalo bulls. A total of 7761 normal semen ejaculates were collected and characterized at ILMTC laboratory from 26 bulls from 2009 to 2019. For VOL, MM, LS, AS, and CONC, the actual means were 3.89 mL, 62.37%, 60.64%, 3.94%, and 0.67 × 109 sperm/mL, respectively. The prediction of breeding values for semen traits was estimated using a Bayesian procedure. The estimated standardized EBVs and phenotypic values were used in the principal component analysis (PCA). Of five PCs, one PC (PC1) had > 1 eigenvalues that was responsible for 87.19% of the total variation of SEBV, and two PCs had > 1 eigenvalues that were responsible for 59.61% and 21.35% of the total variation of the phenotypic values. Together, PC1 and PC2 accounted for 97.97% of the total variance of SEBV and 80.96% of the total variance of phenotypic values. A graphs of the first two components showed the traits separated into two different directions by group. This indicates each group was under similar genetic influence. Therefore, selection can be done separately for each group without influencing the other. Principal component analysis reduced variables to describe the key information in buffalo semen data. [ABSTRACT FROM AUTHOR]

The goal of lipidomic studies is to provide a broad characterization of cellular lipids present and changing in a sample of interest. Recent lipidomic research has significantly contributed to revealing the multifaceted roles that lipids play in fundamental cellular processes, including signaling, energy storage, and structural support. Furthermore, these findings have shed light on how lipids dynamically respond to various perturbations. Continued advancement in analytical techniques has also led to improved abilities to detect and identify novel lipid species, resulting in increasingly large datasets. Statistical analysis of these datasets can be challenging not only because of their vast size, but also because of the highly correlated data structure that exists due to many lipids belonging to the same metabolic or regulatory pathways. Interpretation of these lipidomic datasets is also hindered by a lack of current biological knowledge for the individual lipids. These limitations can therefore make lipidomic data analysis a daunting task. To address these difficulties and shed light on opportunities and also weaknesses in current tools, we have assembled this review. Here, we illustrate common statistical approaches for finding patterns in lipidomic datasets, including univariate hypothesis testing, unsupervised clustering, supervised classification modeling, and deep learning approaches. We then describe various bioinformatic tools often used to biologically contextualize results of interest. Overall, this review provides a framework for guiding lipidomic data analysis to promote a greater assessment of lipidomic results, while understanding potential advantages and weaknesses along the way. [ABSTRACT FROM AUTHOR]

A popular approach to the simulation of multivariate, non-normal data in the social sciences is to define a multivariate normal distribution first, and then alter its lower-dimensional marginals to achieve the shape of the distribution intended by the researchers. A consequence of this process is that the correlation structure is altered, so further methods are needed to specify an intermediate correlation matrix in the multivariate normal distribution step. Most of the techniques available in the literature estimate this intermediate correlation matrix bivariately (i.e., correlation by correlation), risking the possibility of generating a non-positive definite matrix. The present article addresses this issue by offering an algorithm that estimates all elements of the intermediate correlation matrix simultaneously, through stochastic approximation. A small simulation study demonstrates the feasibility of the present method to induce the correlation structure both in simulated and empirical data. [ABSTRACT FROM AUTHOR]

Estimating the individualized treatment effect has become one of the most popular topics in statistics and machine learning communities in recent years. Most existing methods focus on modeling the heterogeneous treatment effects for univariate outcomes. However, many biomedical studies are interested in studying multiple highly correlated endpoints at the same time. We propose a random forest model that simultaneously estimates individualized treatment effects of multivariate outcomes. We consider a popular study design where covariates and outcomes are measured both before and after the intervention. The proposed model uses oblique splitting rules to partition population space to the neighborhood that experiences distinct treatment effects. An extensive simulation study suggests that the proposed method outperforms existing methods in various nonlinear settings. We further apply the proposed method to two nutrition studies investigating the effects of food consumption on gastrointestinal microbiota composition and clinical biomarkers. The method has been implemented in a freely available R package MOTE.RF at https://github.com/boyiguo1/MOTE.RF. [ABSTRACT FROM AUTHOR]

Research into ancient Chinese metallurgy has flourished over recent years with the accumulation of analytical data reflecting the needs of so many archaeological finds. However, the relationship between technology and society is unlikely to be revealed simply by analysing more artefacts. This is particularly evident in the debates over the sources of metals used to manufacture the Chinese ritual bronzes of the Shang (c. 1500-1046 BCE), Western Zhou (c. 1046–771 BCE) and Eastern Zhou (c. 771–256 BCE) dynasties. This article recognises that approaches to analytical data often fail to provide robust platforms from which to investigate metallurgical technology within its wider social and cultural contexts. To address this issue, a recently developed multivariate approach is applied to over 300 Chinese ritual bronzes from legacy data sets and nearly 100 unearthed copper-based objects from Anyang and Hanzhong. Unlike previous investigations that have relied predominantly on interpreting lead isotope signatures, the compositional analyses presented here indicate that copper and lead used to manufacture the bronzes are derived from mining progressively deeper ores in the same deposits rather than seeking out new sources. It is proposed that interpretations of social, cultural and technological change predicated on the acquisition of metals from disparate regions during the Chinese Bronze Age may need to be revised. [ABSTRACT FROM AUTHOR]

The basic random effects meta-analytic model is overwhelmingly dominant in psychological research. Indeed, it is typically employed even when more complex multilevel multivariate meta-analytic models are warranted. In this paper, we aim to help overcome challenges so that multilevel multivariate meta-analytic models will be more often employed in practice. We do so by introducing MLMVmeta—an easy-to-use web application that implements multilevel multivariate meta-analytic methodology that is both specially tailored to contemporary psychological research and easily estimable, interpretable, and parsimonious—and illustrating it across three case studies. The three case studies demonstrate the more accurate and extensive results that can be obtained via multilevel multivariate meta-analytic models. Further, they sequentially build in complexity featuring increasing numbers of experimental factors and conditions, dependent variables, and levels; this in turn necessitates increasingly complex model specifications that also sequentially build upon one another. [ABSTRACT FROM AUTHOR]

Teff [Eragrostis tef (Zuccagni) Trotter] is an indigenous crop in Ethiopia, and Amhara region is the predominant teff producing region in the country. This study was aimed at developing an analytical methodology useful to determine the geographical origin of teff produced in the Amhara Region, based on multielement analysis combined with multivariate statistical techniques. For this, a total of 72 teff grain samples were collected from three zones (West Gojjam, East Gojjam, and Awi) and analysed for K, Na, Mg, Ca, Mn, Cu, Fe, Co, Ni, Zn, Cr, and Cd contents using inductively coupled plasma-optical emission spectroscopy (ICP-OES). The digestion and ICP-OES analysis method were accurate, with percentage recovery ranging 85.5 to 109% across the different metals analysed. Principal component analysis (PCA) and linear discriminant analysis (LDA) were applied to discriminate samples based on their production regions. Magnesium, Ca, Fe, Mn, and Zn were the most discriminating elements among the samples. The LDA model provided 96% correct classification of samples into production regions and varietal types, with an average prediction ability of 92%. Hence, the multielement analysis combined with statistical modeling can be used in the authentication of the geographical origin and varietal type of teff from Amhara region. [ABSTRACT FROM AUTHOR]

This research was carried out to characterize the West African Dwarf (WAD) goats using multivariate approach based on their morpho-biometric traits in the tropical rainforest zone of Nigeria. Data on four physical qualitative traits, six morphology indicators, and nine morphological characters were generated from 279 goats. Descriptive statistics, non-parametric analysis and one-way approach of ANOVA with post hoc tests, discriminant analysis (canonical), principal component analysis (categorical), and regression tree analysis were employed to discern the effects of location and sex on the parameters, as well as characterize the goats. The frequencies of the qualitative traits show black coat color predominated (60.2%) over other coat colors, plain color predominated (75.3%) over other color patterns, straight horns (38.1%) predominate over other horn shape, and beard presence (66.7%) predominated over non-beard presence among the goats in terms of locations and sexes. Location and age were significantly affected on the biometric characters (p<0.05 and p<0.001) with goats from Enugu having higher values than other states, while only head length (HL) was affected significantly by sex (p< 0.05). The morphological indicators were not significantly affected by location and sex (p>0.05 and p>0.001), while age was significant. The discriminant analysis revealed a sparse, non-intermingling populations for the physical traits, biometric characters, and body indices implying they are different distinct populations from each other. Generally, the goat populations could be characterized using heart girth (HG), rump height (RH), and body weight (BW) due to the principal components, while the CHAID and Exhaustive CHAID algorithm analysis revealed BW and HL as the genetic classifiers of the WAD goats, both in terms of locations. Basically, the goats from the 3 locations appeared to be more homogeneous in nature, implying that a deployment of specific complimentary genomics work targeted at future selection and breeding programs to improve their productivity in the tropical rainforest of Nigeria is necessary. [ABSTRACT FROM AUTHOR]

It is common in a number of disciplines such as economics, sociology, psychology and clinical trials that researchers are interested to test treatment effects among several of the outcomes in the same direction. Such tests can be performed by using equi-directional test statistics for multivariate data. If on the other hand, treatment effects with respect to one or more of the outcomes differ in direction, the power of equi-directional tests is compromised. Thus, we interchanged the signs of different outcomes by multiplying the values with - 1 and made the anticipated direction similar. Following this, we employed a recently proposed test statistic which handles equi-directional alternatives since the direction of treatment effects is made uniform through interchanging the signs. Once monotonic trend, that is, monotonic increasing for some of the outcomes and monotonic decreasing for others is demonstrated through the global test statistic, an investigator may be further interested in which specific outcomes or sets of outcomes actually these trends are observed. To address this issue, we adapted a closed testing principle. The whole procedure is illustrated by data sets from a toxicology study carried out by the National Toxicology Program, and a cost of transporting milk from farms to dairy plants per mile by different trucks. [ABSTRACT FROM AUTHOR]

Surface and groundwater are the most common sources of water in Nigeria's rural communities, which are used for a variety of purposes ranging from farming to industrial processes and other domestic household activities including drinking. Water that contains heavy metals in excess of the maximum permitted levels poses a risk to human health. This study aims to evaluate the levels of heavy metals in surface and groundwater in Ifite Ogwari, a rural community in Anambra State, Southeast Nigeria, as well as their ecological indices and human health risks assessment. The concentration levels of Ni, Cr, Cd, Pb, Zn, Fe, Mn, and Cu were determined in fifteen water samples from the major water sources in the study area, viz., streams, river, and hand-dug wells. The water samples were collected using precleaned 500 cm3 glass bottles and were analyzed using Atomic Absorption Spectroscopy (AAS) technique. The results showed that four metals (Cr, Cu, Mn, and Pb) out of the eight heavy metals were not detected in all the samples. The concentration levels of total Ni had a range of 0.029–0.11 mg/L with highest concentration occurring at Isiachala stream, Onowulugbe well, and Omambala river (0.11 mg/L). The Cd levels in the water samples had a range of 0.001–0.036 mg/L, with Isiachala and Iyiutu having the highest values (0.036 mg/L). The concentration of Fe ranged from 0.01 to 0.047 mg/L. Mn was detected at a concentration level of 0.003 mg/L in Iyiutu stream only. The Pearson correlation deduced a strong correlation (> 0.75) and a medium correlation (0.50–0.75) for sample locations and analytes, while three factors (principal component analysis) were produced, which indicates the influence of anthropogenic release rather than natural release. Ecological indices showed the impact of multi-elemental matrices on the ecology, while health risk assessments showed that there was no adverse cancer risk or non-cancer risk across respondents (adults and children). The obtained results showed that anthropogenic release has an extensive mobility influence on the natural level of metals in surface and ground water in Ifite Ogwari, and so proper treatment is advocated. This study has shown that the water sources from Ifite Ogwari pose no adverse health risk to the residents. Consequently, additional research on Ifite Ogwari water is needed to characterize "forever chemicals," per- and polyfluoroalkyl substances (PFAS) which are ubiquitous, cancerous and have been linked to reproductive and immune system harm, and suggest routes for remediation. [ABSTRACT FROM AUTHOR]

Major depressive disorder (MDD) exhibits diverse symptomology and neuroimaging studies report widespread disruption of key brain areas. Numerous theories underpinning the network degeneration hypothesis (NDH) posit that neuropsychiatric diseases selectively target brain areas via meaningful network mechanisms rather than as indistinct disease effects. The present study tests the hypothesis that MDD is a network-based disorder, both structurally and functionally. Coordinate-based meta-analysis and Activation Likelihood Estimation (CBMA-ALE) were used to assess the convergence of findings from 92 previously published studies in depression. An extension of CBMA-ALE was then used to generate a node-and-edge network model representing the co-alteration of brain areas impacted by MDD. Standardized measures of graph theoretical network architecture were assessed. Co-alteration patterns among the meta-analytic MDD nodes were then tested in independent, clinical T1-weighted structural magnetic resonance imaging (MRI) and resting-state functional (rs-fMRI) data. Differences in co-alteration profiles between MDD patients and healthy controls, as well as between controls and clinical subgroups of MDD patients, were assessed. A 65-node 144-edge co-alteration network model was derived for MDD. Testing of co-alteration profiles in replication data using the MDD nodes provided distinction between MDD and healthy controls in structural data. However, co-alteration profiles were not distinguished between patients and controls in rs-fMRI data. Improved distinction between patients and healthy controls was observed in clinically homogenous MDD subgroups in T1 data. MDD abnormalities demonstrated both structural and functional network architecture, though only structural networks exhibited between-groups differences. Our findings suggest improved utility of structural co-alteration networks for ongoing biomarker development. [ABSTRACT FROM AUTHOR]

Screening of wheat genotypes containing suitable candidate genes for salt tolerance is an essential step in stress-breeding. This study aimed to determine genetic variation, and screen salt-tolerant genotypes using morphological and molecular markers as selection criteria. In this study, 44 genotypes of wheat were screened at the seedling stage by hydroponic culture. The treatments included a control (1 dSm−1), and low (9 dSm−1), moderate (12 dSm−1) and high (15 dSm−1) salinity stresses. Moreover, these genotypes were subjected to marker-assisted selection using 15 salt tolerance related gene-linked simple sequence repeats (SSRs). The total salt stress response indices (TSSRI) categorized wheat genotypes into four groups; tolerant, moderately tolerant, moderately susceptible, and susceptible. TSSRI and principal component analysis (PCA) revealed that Akbar was the most salt-tolerant wheat genotype, followed by Barigom-20, Barigom-22, BW-1284, BW-1262, BW-1237, and Barigom-24. It was evident that the growth of most of the genotypes was significantly inhibited by salt treatment. The tolerant genotypes, however, had to face a lower reduction in morphological parameters. The highest value (0.7127) of polymorphism information content (PIC) was shown by the marker Xwmc17. UPGMA dendrogram showed a clear differentiation of 43 wheat genotypes into seven major clusters considering genetic similarity. Cluster-I consisted of twelve genotypes, seven of which were found salt-tolerant, cluster-VI was composed of seven genotypes, of which four emerged as salt-tolerant, and Cluster-VII accommodated three genotypes all of which were tolerant based on TSSRI. On the contrary, seven genotypes in Cluster-II, and five in Cluster-III were found salt-susceptible according to phenotypic assessment at the seedling stage. In conclusion, considering the morphological characterization and molecular markers response, Akbar, Barigom-20, Barigom-22, BW-1284, and BW-1262 are identified as tolerant genotypes. Therefore, these screened genotypes could be utilized as valuable genetic resources in wheat breeding for salinity tolerance research. [ABSTRACT FROM AUTHOR]

Given gridded cell-average data of a smooth multivariate function, we present a constructive explicit procedure for generating a high-order global approximation of the function. One contribution is the derivation of high-order approximations to point-values of the function directly from the cell-average data. The second contribution is the development of univariate B-spline-based high-order quasi-interpolation operators using cell-average data. Multivariate spline quasi-interpolation approximation operators are obtained by tensor product of the univariate operators. [ABSTRACT FROM AUTHOR]

The multivariate probit is popular for modeling correlated binary data, with an attractive balance of flexibility and simplicity. However, considerable challenges remain in computation and in devising a clear statistical framework. Interest in the multivariate probit has increased in recent years. Current applications include genomics and precision medicine, where simultaneous modeling of multiple traits may be of interest, and computational efficiency is an important consideration. We propose a fast method for multivariate probit estimation via a two-stage composite likelihood. We explore computational and statistical efficiency, and note that the approach sets the stage for extensions beyond the purely binary setting. [ABSTRACT FROM AUTHOR]

Camouflaged object detection (COD) aims to find objects hidden in the surrounding environment. Camouflaged objects are usually fused with the background and have high intrinsic similarity, and the sizes and appearances of camouflaged objects vary, causing most previous single-stage methods to fail. To this end, we propose a multivariate feature interaction network (MFINet) based on a multistage design. Specifically, the MFINet consists of three stages: a feature exploration stage, a feature fusion stage, and a feature refinement stage. In the feature exploration phase, we design three key components: a global information exploration unit (GIEU), a spatial information exploration unit (SIEU), and a multi-scale exploration unit (MEU). The GIEU locates the position of the camouflaged object by integrating the global contextual information. The SIEU aggregates effective spatial information. The MEU explores the rich multi-scale information. In the feature fusion stage, we design a multielement feature fusion module to integrate the consistency and complementarity of multi-scale features to fully explore the valuable clues in the context by exploiting the global semantics and spatial details. In the feature refinement phase, we design a cross-layer consistency refinement module to facilitate information transfer between neighboring layers and refine camouflage features by mutual guidance of low-level and high-level features. The proposed MFINet is evaluated on four challenging COD benchmark datasets (i.e., CAMO, CHAMELEON, COD10K, and NC4K) using four widely used metrics. These evaluations demonstrate that our proposed method is superior to other existing cutting-edge methods for camouflaged object detection. The code will be released at https://github.com/ZYJ9224/MFINet. [ABSTRACT FROM AUTHOR]

The collection of sensor-based data is dramatically increased in the mining industry. One of the widely used applications of the collected data is to identify rock domains and to estimate rock mass properties. Rock domaining and classification are usually done by engineers and geologists based on manual downhole logging and using engineering judgments and expert interpretation. However, such human-centered processes are time-consuming, costly, and often have low precision as they are subjective to personal interpretation. On the other hand, with the emerging of real-time recording technologies on drilling equipment through sensors, it is necessary to distinguish the rock boundaries in real time so that the recorded data can be used to optimize decisions. The main objective of this study is, therefore, to develop approaches to automatically determine rock domains in mining operations by using a hybrid recurrence quantification analysis (RQA), quadrant scan (QS), and machine learning (ML) methods to both recognize the boundaries of rock domains and to classify the rock masses based on their physicochemical features. The proposed method was applied on a multivariate geochemical assay dataset of an iron ore mine, and the results were evaluated against the downhole logging recorded by geologists and the wavelet transform method as a comparative analysis. Results show that by using RQA, QS, and ML techniques, it is reasonably possible to quickly determine the rock domains and to characterize them according to their physicochemical characteristics. [ABSTRACT FROM AUTHOR]

There are several different topics that can be addressed with multivariate failure time regression data. Data analysis methods are needed that are suited to each such topic. Specifically, marginal hazard rate models are well suited to the analysis of exposures or treatments in relation to individual failure time outcomes, when failure time dependencies are themselves of little or no interest. On the other hand semiparametric copula models are well suited to analyses where interest focuses primarily on the magnitude of dependencies between failure times. These models overlap with frailty models, that seem best suited to exploring the details of failure time clustering. Recently proposed multivariate marginal hazard methods, on the other hand, are well suited to the exploration of exposures or treatments in relation to single, pairwise, and higher dimensional hazard rates. Here these methods will be briefly described, and the final method will be illustrated using the Women's Health Initiative hormone therapy trial data. [ABSTRACT FROM AUTHOR]

Objective: Glioblastoma Multiforme (GBM), a devastating the most common primary malignant intracranial brain tumors. In India, the incidence of this malignancy is escalating, however, there are very few studies on this tumor entity from Indian population. The present study sought to investigate the prevalence and prognostic significance of Signal Transducer and Activator of Transcription 3 (STAT3) gene expression in GBM patients from Western India. Method: STAT3 gene expression using real-time PCR was detected in total 55 GBM patients. The impact of STAT3 aberrant expression on progression-free survival (PFS) and overall (OS) was analysed using univariate and multivariate survival analysis. The data were analysed using SPSS statistical software and p value ≤0.05 was considered as significant. Results: The aberrant STAT3 expression was found in 85% (47/55) of patients with -1.12 fold change down-regulation in 49% (23/47) and 3.36 fold change up-regulation was noted in 51% (24/47) of patients. In wild type IDH tumors (n=30), down regulation and up regulation of STAT3 was noted in 63% and 27% of patients, respectively, whereas, for IDH mutant GBM tumors (n=25), the incidence of low expression and high expression of STAT3 was noted in 16% and 68% of patients, respectively. Thus, we found that incidence of STAT3 down regulation was significantly high in patients with IDH wild type tumors, whereas, in IDH mutant GBM tumors, the incidence of up-regulated STAT3 was significantly high (P=0.021, χ2=12.81, r=+0.310). In Kaplan-Meier univariate survival analysis, a part from age (P=0.006), tumor location (P=0.025), and KPS score (P=0.002), co-detection of STAT3 up regulation and presence of IDH mutation (P=0.030) remained significant prognostic factors for PFS and OS. In multivariate survival analysis also, co-detection of STAT3 high expression and presence of IDH mutation remained independent prognosticators for PFS (HR=6.45, 95% CI=1.32-31.40, P=0.021) and OS (HR=8.69, 95% CI=1.66-45.51, P=0.010). Conclusion: For GBM tumors, STAT3 up-regulation and presence of IDH mutations together predicts better survival. This reflects unique molecular etiology for GBM patients. Therefore, they would be useful in the future for targeted therapy and for clinicians they would be useful for better patient management. However, study on a larger sample size is required for validation. [ABSTRACT FROM AUTHOR]

Natural processes and anthropogenic activities are the major factors that can deteriorate groundwater quality. This study provides an insight into groundwater quality and its drinking suitability in part of Rajasthan. The physicochemical analysis of parameters (pH, EC, TDS, Ca2+, Mg2+, Na+, K+, F−, HCO3−, Cl−, NO3−, and SO42−) was conducted in the laboratory. High alkalinity and hardness were significant problems in most of the groundwater samples. The maximum concentration observed for F− and NO3− was 3.8 mg/L and 110.70 mg/L, and around 27.66% and 12.76% of samples, respectively, were above the WHO permissible limits. The dominant natural processes affecting groundwater quality were assessed through chemometric analysis, geochemical modeling, and conventional plots. Carbonate dissolution, reverse ion exchange, silicate weathering, evaporite dissolution, and rock-water interaction were the dominant processes responsible for groundwater's chemical characteristics. However, anthropogenic activities like improper drainage of sewage and fertilizer use in agriculture also impact groundwater quality. Cluster analysis resulted in 4 groups representing specific geochemical properties within each group. Entropy-based weights of the water quality parameters were used to calculate the entropy water quality index (EWQI) to assess groundwater suitability for drinking. Nearly 25.53% of samples (EWQI > 100) were unsuitable for drinking. Policymakers can assess groundwater suitability using EWQI to prioritize the interventions for providing suitable quality water for domestic use. This study's findings are relevant from the public health perspective and in understanding water quality parameter's spatial distribution. [ABSTRACT FROM AUTHOR]

Being able to interpret a model's predictions is a crucial task in many machine learning applications. Specifically, local interpretability is important in determining why a model makes particular predictions. Despite the recent focus on interpretable Artificial Intelligence (AI), there have been few studies on local interpretability methods for time series forecasting, while existing approaches mainly focus on time series classification tasks. In this study, we propose two novel evaluation metrics for time series forecasting: Area Over the Perturbation Curve for Regression and Ablation Percentage Threshold. These two metrics can measure the local fidelity of local explanation methods. We extend the theoretical foundation to collect experimental results on four popular datasets. Both metrics enable a comprehensive comparison of numerous local explanation methods, and an intuitive approach to interpret model predictions. Lastly, we provide heuristical reasoning for this analysis through an extensive numerical study. [ABSTRACT FROM AUTHOR]

Predicting the number of COVID-19 cases in a geographical area is important for the management of health resources and decision making. Several methods have been proposed for COVID-19 case predictions but they have important limitations in terms of model interpretability, related to COVID-19's incubation period and major trends of disease transmission. To be able to explain prediction results in terms of incubation period and transmission trends, this paper presents the Multivariate Shapelet Learning (MSL) model to learn shapelets from historical observations in multiple areas. An experimental evaluation was done to compare the prediction performance of eleven algorithms, using the data collected from 50 US provinces/states. Results show that the proposed method is effective and efficient. The learned shapelets explain increasing and decreasing trends of new confirmed cases, and reveal that the COVID-19 incubation period in the USA is around 28 days. [ABSTRACT FROM AUTHOR]

The natural resources, especially water in the Densu river basin, play significant roles in the socio-economic development of Ghana. The purpose of this study was to analyse the water quality of the Densu river using water quality index (WQI) and multivariate techniques. In this study, physico-chemical and bacteriological parameters were measured from surface water samples taken from eight (8) sampling stations in the study area. water quality index and multivariate techniques such as hierarchical cluster analysis and principal component analysis were utilized in the analysis of surface water quality data. The results indicated that the average WQI of the Densu river for the two sampling periods was sixty-one (61) which is classified as Medium, based on the Solway WQI index. The pH levels of all the samples were within allowable limits of World Health Organization (WHO) guidelines. All the sampling stations for the two seasonal periods had bacteriological parameters higher than WHO guidelines, making the samples unsuitable for most domestic uses. The study revealed that six (6) principal components accounted for about 97% of the total variance of dataset and three (3) spatial clusters were classified. This research has provided the basis for applying both WQI and multivariate techniques in analysing and classifying water quality in a river basin. [ABSTRACT FROM AUTHOR]

Surgical site infection (SSI) is a challenging and resource-consuming healthcare problem. Predicting the onset of SSI beforehand can help prevent or minimize its incidence. The present study aimed to determine the independent predictors of incisional SSI after open appendectomy using a multivariate analysis and to establish a predictive risk score of SSI after appendectomy. Records of eligible patients who underwent open appendectomy were reviewed. The characteristics and treatment outcomes of patients were collected and analyzed. Significant association between different variables and SSI after appendectomy was examined by univariate analysis. Then, variables with a significant association with SSI were entered into a multivariate binary logistic regression analysis to determine the significant independent predictors of SSI. The study included 343 patients (51.3% female). Incisional SSI was recorded in 44 (12.8%) patients. Univariate analysis revealed five parameters with a significant association with SSI, including BMI > 30 kg/m2 (p < 0.0001), diabetes mellitus (DM) (p = 0.0001), total leukocyte count (p = 0.04), free intraperitoneal fluid (p < 0.0001), and perforated/gangrenous appendicitis (p < 0.0001). After identifying four significant independent predictors of incisional SSI by binary logistic regression analysis, a predictive risk score was developed. The independent predictors of SSI were DM (OR = 6.05, p = 0.005), free intraperitoneal fluid (OR = 6.94, p = 0.0001), obesity (OR = 8.94, p = 0.0001), and perforated/gangrenous appendicitis (OR = 24.64, p = 0.0001). Four independent predictors of incisional SSI after open appendectomy were found after multivariate analysis. A predictive risk score of SSI was developed and included obesity, DM, free intraperitoneal fluid, and perforated/gangrenous appendicitis as predictors of SSI. [ABSTRACT FROM AUTHOR]

Hydrochemistry, sources of ions, identification of pollution hotspot, and potability of groundwater in shallow and deep aquifers of Palakkad and Chittur taluks (area = 1875 km2) of Bharathapuzha river basin, the largest river basin in Kerala, India, are discussed in this paper. The study area belongs to one of the rice bowls of the State. Geologically, the study area consists of Archaean crystallines, overlaid by laterite. Due to the unique geography, subhumid to semiarid climate, and intense agricultural activities, the quality of groundwater is a serious concern and warrants attention. For delineating the major ions and nutrients, water samples were analyzed for shallow (dug wells) and deep (bore wells) aquifers during monsoon (MON) and postmonsoon (POM) seasons. Physicochemical parameters showed distinct spatiotemporal variation and quality degradation, which was more prominent in deep groundwater. The relative abundance of cations and anions is in the order Na > Ca > Mg > K and HCO3 > Cl > CO3 for both the aquifers. The spatial distribution trend of parameters in deep and shallow aquifers showed a similar pattern indicating a common influence on water quality. Multivariate factor analysis (R-mode) employed in the study derived three main factors controlling the groundwater chemistry, viz. geogenic, anthropogenic, and pH. Among these, water chemistry is chiefly controlled by the geogenic factor and partly by the other two. It is also reflected well in the correlation analysis of various parameters. Since the eastern part of Chittur taluk in the study area is more impacted by anthropogenic and climatic influences, a pollution hotspot has been identified in this region through the application of Geographic Information System (GIS) overlay analysis. Thus, the study reveals agrochemical, climatic, and geologic influences on the chemistry and quality of groundwater. [ABSTRACT FROM AUTHOR]

Coal mining activities have been ongoing in Okaba for years without adequate attention paid to pollution and reclamation. Soil and plants were sampled from Okaba coal mine to determine the metal content in the soil and to identify the plants that have potential for phytoremediation. The samples were prepared, separated into various tissues, and labeled and taken to the laboratory. Digestion was carried out using acid mixture of nitric acid (HNO3): perchloric acid (HCLO4) for the plants, while the mixture of nitric acid (HNO3): hydrochloric acid (HCl): perchloric acid (HCLO4) was used for the soil. The concentrations of metals from the samples were determined using EDX3600B X-ray fluorescence spectrometer (Sky ray Instruments Inc., USA), a World Bank Assisted Project, Nigeria. Bioconcentration factor (BCF), translocation factor (TF), and bioaccumulation coefficient (BAC) or extraction coefficient (EC) were determined to identify the plants that have potential for hyperaccumulation, phytoextraction, and phytostabilization and ability to store up these metals for harvesting. Hierarchical cluster analysis was performed to highlight the relationship between the variables. This study has revealed that Ageratum conyzoides Linn (ACL), Desmodium velutinum (DV), Triumfetta rhiomdoidea Jacq. (TRJ), Gleichenia linearis (Burns) (GL), Selaginella myosurus (SM), and Sida linifolia juss.excav. (SL) had potential as hyperaccumulators of nickel. Eleven (11) plants have potential for phytoextraction of manganese (Mn), and six (6) plants have stabilization potential for the same metal. Eight (8) phytoextractors and seven (7) stabilizers plants were recorded for Ni. Eclipta alba (L) Hassk (EAH) (location 4) and TRJ were phytostabilizers plants for Pb. Six (6) other plants have potential as phytoextractors of Pb. Eight (8) of the studied plants were suitable for phytoextraction of Ni, and five (5) plants were suitable as phytostabilizers. Most of the plants were found to be useful for phytoremediation of the soil. [ABSTRACT FROM AUTHOR]

Empathy, among other social-cognitive processes, changes across adulthood. More specifically, cognitive components of empathy (understanding another's perspective) appear to decline with age, while findings for affective empathy (sharing another's emotional state) are rather mixed. Structural and functional correlates underlying cognitive and affective empathy in aging and the extent to which valence affects empathic response in brain and behavior are not well understood yet. To fill these research gaps, younger and older adults completed a modified version of the Multifaceted Empathy Test, which measures both cognitive and affective empathy as well as empathic responding to both positive and negative stimuli (i.e., positive vs. negative empathy). Adopting a multimodal imaging approach and applying multivariate analysis, the study found that for cognitive empathy to negative emotions, regions of the salience network including the anterior insula and anterior cingulate were more involved in older than younger adults. For affective empathy to positive emotions, in contrast, younger and older adults recruited a similar brain network including main nodes of the default mode network. Additionally, increased structural microstructure (fractional anisotropy values) of the posterior cingulum bundle (right henisphere) was related to activation of default mode regions during affective empathy for positive emotions in both age groups. These findings provide novel insights into the functional networks subserving cognitive and affective empathy in younger and older adults and highlight the importance of considering valence in empathic response in aging research. Further this study, for the first time, underscores the role of the posterior cingulum bundle in higher-order social-cognitive processes such as empathy, specifically for positive emotions, in aging. [ABSTRACT FROM AUTHOR]

Assessment of the spatial probability of future landslide occurrences for disaster risk reduction is done through landslide susceptibility modelling. In this study, we investigated the effect of time and space partitioning strategies of samples on the performance of regional landslide susceptibility models on macro-scale mapping in the state of Mizoram, India, covering 21,087 km2 area. We used landslide inventory data of 2014 and 2017 periods consisting of 1205 and 2265 landslides, respectively, to train and test the models with four sampling strategies such as spatial, temporal, temporal (size constrained) and temporal (geographic constrained). We used five commonly inherited models such as multiclass weighted overlay (MCWO), information value (IV), weights of evidence (WoE), logistic regression (LR) and artificial neural network (ANN) to evaluate the effect of sampling strategies on the model performance for regional landslide susceptibility mapping. Validation of model performance was done using receiver operating characteristic (ROC) curve. Traditional spatial sampling strategy applied to landslides in 2014 with a random split in 70:30 proportion provided a high performance of all the five models but failed to predict landslides in 2017. The landslide incidences in 2017, when used for model validation either entirely or in different split conditions (both size and geographic constrained), provided consistent performance, even though the testing sample size is large or have a different spatial disposition, if the training was carried out with non-linear susceptibility models such as LR and ANN using landslide incidences in 2014. Results show the importance of sample selection during validation of landslide susceptibility models on a regional scale. [ABSTRACT FROM AUTHOR]

We propose the class of asymmetric vector moving average (asVMA) models. The asymmetry of these models is characterized by different MA filters applied to the components of vectors of lagged positive and negative innovations. This allows for a detailed investigation of the interrelationships among past model innovations of different sign. We derive some covariance matrix properties of an asVMA model under the assumption of Gaussianity. Related to this, we investigate the global invertibility condition of the proposed model. The paper also introduces a maximum likelihood estimation procedure and a multivariate Wald-type test statistic for symmetry versus the alternative of asymmetry. The finite-sample performance of the proposed multivariate test is studied by simulation. Furthermore, we devise an exploratory test statistic based on lagged sample cross-bicovariance estimates. The estimation and testing procedures are used to uncover asymmetric effects in two US growth rates, and in three US industrial prices. [ABSTRACT FROM AUTHOR]

The Niagara River, which connects two Great Lakes (Erie and Ontario) and forms a border between Canada and the United States, has experienced decades of abiotic and biotic disturbance as well as long-term restoration efforts. Given the iconic riverscape and importance as a binational fisheries resource, a biodiversity assessment of the mainstem Niagara River fish assemblage is overdue. Here, fish assemblage and habitat data from a standardized boat electrofishing program of the Niagara River were combined with species trait data related to substrate associations, diet preferences, reproductive strategies, and body size to quantify biodiversity patterns among river sections (sites above and below Niagara Falls), seasons (spring, summer, fall), and years (2015–2017). Sixty-five species were captured representing a variety of trait combinations. Significant differences in functional dispersion and divergence (i.e., functional diversity) were observed between river sections, seasons, and (or) years. The fish community captured in the lower river in spring 2015 had both the highest average functional dispersion (2.08 ± 0.32 SD) and divergence (0.88 ± 0.04 SD) compared to the other seasonal sampling efforts, but relatively few fishes were captured (n = 686). Although non-native fishes represented a small portion of the catch over the 3 years (8.6% of catch), the seasonal presence (spring and fall) of mostly introduced large-bodied salmonids expanded functional trait space in the lower river during these periods. The importance of rare species on functional diversity metrics suggests further insight on local species detection probabilities is needed to understand if differences in functional diversity reflect ecological patterns or are driven by sampling design. [ABSTRACT FROM AUTHOR]

In this article, we present algebraic attacks against the Extension Field Cancellation (EFC ) scheme, a multivariate public-key encryption scheme which was published at PQCRYPTO'2016. First, we present a successful Gröbner basis message-recovery attack on the first and second proposed parameters of the scheme. For the first challenge parameter, a Gröbner-based hybrid attack has a 2 65 bit complexity which beats the claimed 80 bit security level. We further show that the algebraic system arising from an EFC public-key is much easier to solve than a random system of the same size. Briefly, this is due to the apparition of many lower degree equations during the Gröbner basis computation. We present a polynomial-time method to recover such lower-degree relations and also show their usefulness in improving the Gröbner basis attack complexity on EFC . Thus, we show that there is an algebraic structural weakness in the system of equations coming from EFC and hence makes the scheme not suitable for encryption. [ABSTRACT FROM AUTHOR]

Introduction: Manganese is important for the endocarditis pathogen Streptococcus sanguinis. Little is known about why manganese is required for virulence or how it impacts the metabolome of streptococci. Objectives: We applied untargeted metabolomics to cells and media to understand temporal changes resulting from manganese depletion. Methods: EDTA was added to a S. sanguinis manganese-transporter mutant in aerobic fermentor conditions. Cell and media samples were collected pre- and post-EDTA treatment. Metabolomics data were generated using positive and negative modes of data acquisition on an LC–MS/MS system. Data were subjected to statistical processing using MetaboAnalyst and time-course analysis using Short Time series Expression Miner (STEM). Recombinant enzymes were assayed for metal dependence. Results: We observed quantitative changes in 534 and 422 metabolites in cells and media, respectively, after EDTA addition. The 173 cellular metabolites identified as significantly different indicated enrichment of purine and pyrimidine metabolism. Further multivariate analysis revealed that the top 15 cellular metabolites belonged primarily to lipids and redox metabolites. The STEM analysis revealed global changes in cells and media in comparable metabolic pathways. Glycolytic intermediates such as fructose-1,6-bisphosphate increased, suggesting that enzymes that utilize them require manganese for activity or expression. Recombinant enzymes were confirmed to utilize manganese in vitro. Nucleosides accumulated, possibly due to a blockage in conversion to nucleobases resulting from manganese-dependent regulation. Conclusion: Differential analysis of metabolites revealed the activation of a number of metabolic pathways in response to manganese depletion, many of which are connected to carbon catabolite repression. [ABSTRACT FROM AUTHOR]

In meta-analysis, primary studies often include multiple, dependent effect sizes. Several methods address this dependency, such as the multivariate approach, three-level models, and the robust variance estimation (RVE) method. As for today, most simulation studies that explore the performance of these methods have focused on the estimation of the overall effect size. However, researchers are sometimes interested in obtaining separate effect size estimates for different types of outcomes. A recent simulation study (Park & Beretvas, 2019) has compared the performance of the three-level approach and the RVE method in estimating outcome-specific effects when several effect sizes are reported for different types of outcomes within studies. The goal of this paper is to extend that study by incorporating additional simulation conditions and by exploring the performance of additional models, such as the multivariate model, a three-level model that specifies different study-effects for each type of outcome, a three-level model that specifies a common study-effect for all outcomes, and separate three-level models for each type of outcome. Additionally, we also tested whether the a posteriori application of the RV correction improves the standard error estimates and the 95% confidence intervals. Results show that the application of separate three-level models for each type of outcome is the only approach that consistently gives adequate standard error estimates. Also, the a posteriori application of the RV correction results in correct 95% confidence intervals in all models, even if they are misspecified, meaning that Type I error rate is adequate when the RV correction is implemented. [ABSTRACT FROM AUTHOR]

Physical frailty and sarcopenia (PF&S) is a prototypical geriatric condition characterized by reduced physical function and low muscle mass. The aim of the present study was to provide an initial selection of biomarkers for PF&S using a novel multivariate analytic strategy. Two-hundred community-dwellers, 100 with PF&S and 100 non-physically frail, non-sarcopenic (nonPF&S) controls aged 70 and older were enrolled as part of the BIOmarkers associated with Sarcopenia and Physical frailty in EldeRly pErsons (BIOSPHERE) study. A panel of 74 serum analytes involved in inflammation, muscle growth and remodeling, neuromuscular junction damage, and amino acid metabolism was assayed. Biomarker selection was accomplished through sequential and orthogonalized covariance selection (SO-CovSel) analysis. Separate SO-CovSel models were constructed for the whole study population and for the two genders. The model with the best prediction ability obtained with the smallest number of variables was built using seven biomolecules. This model allowed correct classification of 80.6 ± 5.3% PF&S participants and 79.9 ± 5.1% nonPF&S controls. The PF&S biomarker profile was characterized by higher serum levels of asparagine, aspartic acid, and citrulline. Higher serum concentrations of platelet-derived growth factor BB, heat shock protein 72 (Hsp72), myeloperoxidase, and α-aminobutyric acid defined the profile of nonPF&S participants. Gender-specific SO-CovSel models identified a "core" biomarker profile of PF&S, characterized by higher serum levels of aspartic acid and Hsp72 and lower concentrations of macrophage inflammatory protein 1β, with peculiar signatures in men and women. SO-CovSel analysis allowed identifying a set of potential biomarkers for PF&S. The adoption of such an innovative multivariate approach could help address the complex pathophysiology of PF&S, translate biomarker discovery from bench to bedside, and unveil novel targets for interventions. [ABSTRACT FROM AUTHOR]

Manually interpreting multivariate drill hole data is very time-consuming, and different geologists will produce different results due to the subjective nature of geological interpretation. Automated or semi-automated interpretation of numerical drill hole data is required to reduce time and subjectivity of this process. However, results from machine learning algorithms applied to drill holes, without reference to spatial information, typically result in numerous small-scale units. These small-scale units result not only from the presence of very small rock units, which may be below the scale of interest, but also from misclassification. A novel method is proposed that uses the continuous wavelet transform to identify geological boundaries and uses wavelet coefficients to indicate boundary strength. The wavelet coefficient is a useful measure of boundary strength because it reflects both wavelength and amplitude of features in the signal. This means that boundary strength is an indicator of the apparent thickness of geological units and the amount of change occurring at each geological boundary. For multivariate data, boundaries from multiple variables are combined and multiscale domains are calculated using the combined boundary strengths. The method is demonstrated using multi-element geochemical data from mineral exploration drill holes. The method is fast, reduces misclassification, provides a choice of scales of interpretation and results in hierarchical classification for large scales where domains may contain more than one rock type. [ABSTRACT FROM AUTHOR]

Thirteen qualitative and six morphometric variables on a total of 651 adult cattle (76 oxen and 575 cows) from four purposively selected districts were recorded to characterize the cattle populations in and around the breeding tract of Raya cattle. General linear model, frequency, and multivariate analysis procedures of Statistical Analysis Software (SAS 9.0) were used to analyze the data by splitting and merging the sexes. Higher measurement values for oxen were recorded over the cows. Location affects the studied traits significantly. The cattle population from Habru district was significantly differentiated from Raya cattle. Among the districts of Raya cattle, lowest values for most of the linear measurements were observed in Kobo district. Majority of the Raya cattle possess upright, lyre-shaped horn, small, and erected hump placed at the cervical thoracic position, large dewlap, naval flap, and perpetual sheath sizes and dominantly dark red body color with uniform body color pattern. Stepwise discriminant function analysis reveals horn length, ear length, and height at wither were the first three most important morphometric variables used in discriminating the cattle populations. Canonical discrimination analysis showed that the first canonical structure explains majority (94.13%) of the total variation with eigenvalue of 2.07 and canonical correlation of 82.14%. Discriminant function analysis showed the classification of an average 61% of the studied animals into their respective districts. Pairwise Mahalanobis distances between populations from different districts were found to be highly significant. Habru cattle population relates distantly from Raya cattle except its short distance with Kobo district. The shortest distance was observed between Raya cattle of Alamata and Raya Azebo districts. In conclusion, cattle from Alamata, Raya Azebo, and Kobo districts can be categorized as Raya cattle, while strong similarities between the first two districts in showing the real characteristics and morphology of Raya cattle were also revealed. [ABSTRACT FROM AUTHOR]

Multivariate generalizability theory (mG-theory) is an important framework in many behavioral and educational studies, as it describes useful psychometric properties of multidimensional assessments. Nevertheless, the use of mG-theory estimation is limited due to the lack of available software for carrying out the necessary calculations: users rely heavily on independent software programs such as mGENOVA and the BUGS/JAGS suite of programs. Considering the prevalence of R software, this paper presents a solution using the glmmTMB package to accomplish the estimation task. Users adopting the proposed method may find it more convenient for conducting both applied investigation and simulation studies without the need to switch between different software programs. [ABSTRACT FROM AUTHOR]

Vector visibility graph (VVG) is an algorithm that transforms multivariate time series into directed complex networks. However, at present, the researches of VVG mainly focus on its degree distribution. Considering the limitation of using the degree distribution of vector visibility graph alone to analyze the complexity of multivariate time series, we use the normalized Shannon entropy and the statistical complexity measure to analyze the complexity of multivariate time series based on the results of the degree distribution. We introduce the multivariate multiscale entropy plane to measure the dynamical complexity of multivariate systems. The effectiveness of the proposed method is validated by numerical simulation from several kinds of systems. In addition, we also observe that it is immune to different levels of noise in a wide range. Then, it is applied to evaluate the dynamic classification of financial time series from stock markets. Our results indicate that this method is effective to research the physical structures of stock markets. [ABSTRACT FROM AUTHOR]

Rainfall-induced landslides are a frequent and often catastrophic geological disaster, and the development of accurate early warning systems for such events is a primary challenge in the field of risk reduction. Understanding of the physical mechanisms of rainfall-induced landslides is key for early warning and prediction. In this study, a real-time multivariate early warning method based on hydro-mechanical analysis and a long-term sequence of real-time monitoring data was proposed and verified by applying the method to predict successive debris flow events that occurred in 2017 and 2018 in Yindongzi Gully, which is in Wenchuan earthquake region, China. Specifically, long-term sequence slope stability analysis of the in situ datasets for the landslide deposit as a benchmark was conducted, and a multivariate indicator early warning method that included the rainfall intensity-probability (I-P), saturation (Si), and inclination (Ir) was then proposed. The measurements and analysis in the two early warning scenarios not only verified the reliability and practicality of the multivariate early warning method but also revealed the evolution processes and mechanism of the landslide-generated debris flow in response to rainfall. Thus, these findings provide a new strategy and guideline for accurately producing early warnings of rainfall-induced landslides. [ABSTRACT FROM AUTHOR]

Incidences of holdouts, as group events, have appeared most frequently in many countries. The underlying cause of these occurrences of high frequency has been closely related to multiple objectives or various conflicts of interest of stakeholders. Each negotiation of holdout demolition represents a typical group consensus problem with the outcome greatly influenced by multiple objectives or variables of the decision makers. In order to effectively deal with such difficult problems, we construct a multivariate, minimum cost consensus model based on interval number programming constrained with random chances by jointly employing various approaches, such as minimum cost consensus model, multivariate planning, stochastic opportunity constrained programming and interval numbers. After the theoretical development, this paper employs the established method to solve the problem of holdout demolition of particular town A. [ABSTRACT FROM AUTHOR]

Executive dysfunctions are common in individuals with Traumatic Brain Injury (TBI). However, change in functional neural coupling of default and executive networks in the post-acute phase (≥ 1 month after injury) patients over time has yet to be understood. During a 5-week observation period, we examined changes in the goal-oriented executive function networks in 20 TBI participants, using a face/scene matching 1-back fMRI task (Chen et al. 2011). We conducted multivariate pattern analysis to assess working memory and visual selective attention, followed by a repeat-measures ANOVA to examine longitudinal changes, with a cluster FDR at p =.001. Results showed that task accuracy significantly improved after follow-up. Significantly increased activity patterns over time were observed in the right dorsolateral prefrontal cortex and right insula. Decreased activity patterns were seen in the left posterior cingulate cortex (PCC), bilateral precuneus, right inferior occipital gyrus and right temporo-occipital junction. Improvement in task accuracy correlated with decreased activity patterns in the PCC (r = −0.478, p = 0.031) and temporo-occipital junction (r = −0.592, p = 0.006), which were interpreted as neural plastic changes. However, we did not observe the default mode network (DMN)-executive network decoupling during task performance that is found in other studies. These results suggest that fMRI of attentional task performance could serve as a potential biomarker for neural plasticity of selective attention in TBI patients in the post-acute phase. [ABSTRACT FROM AUTHOR]

Higher-order information of moving objects is of great importance for processing in-case scenarios in emergency management. Multivariate higher-order information management is a crucial key to the success of emergency management since emergency management involves developing plans with a given set of multiple resources. Past studies focus on univariate higher-order information limiting the scope of applicability and usability. This paper proposes a set of visual analytical approaches supporting multivariate higher-order information for dynamically moving disasters. We introduce a robust Voronoi-based data structure supporting multivariate datasets and dynamic disasters and propose visual analytical approaches for effective emergency management. The proposed visual analytical suite facilitates interactivity and enables users to explore in-case scenarios with multivariate datasets and dynamic disasters. A case study with real datasets is given to explain the applicability, usability and practicability of the proposed system. [ABSTRACT FROM AUTHOR]

In an effort to determine the reason behind excellent nitrate remediation capacity at Kelantan region, a multivariate approach is employed to evaluate extent to which the influence of sea on soil geochemical composition affect variation pattern of groundwater quality. The results obtained from geochemical analysis of paleo-beach soil in coastal site at Bachok revealed multiple redox activity at different soil strata, involving both heterotrophic and autotrophic denitrification. In soil and water analysis, eight of the fourteen hydro-geochemical parameters (conductivity, temperature, soil texture, oxidation reduction potential, pH, total organic carbon, Fe, Cu, Mn, Cl−, SO42−, NO2−, NO3− and PO43−) measured using standard procedures were subjected to multivariate analysis. Evaluation of general variation pattern across the area reveals that the principal component analysis (PCA), hierarchical cluster analysis (HCA) and linear discriminant analysis (LDA) are in consonance with one another on apportioning three parameters (SO42−, Cl− and conductivity) to the coastal sites and two parameters (Fe and NH4+ or NO3−) to inland sites. The step forward analysis of LDA reveals four parameters in order of decreasing significance as Cl−, Fe and SO42−, while the two-way HCA identifies three clusters on location basis, respectively. In addition to the significant data reduction obtained, the results indicate that proximity to sea and location/geological-based influence are more significant than temporal-based influence in denitrification. By extension, the research reveals that influence of labile portion of natural resources is explorable for broader application in other remediation strategies. [ABSTRACT FROM AUTHOR]

Interval-censored failure times arise when the status with respect to an event of interest is only determined at intermittent examination times. In settings where there exists a sub-population of individuals who are not susceptible to the event of interest, latent variable models accommodating a mixture of susceptible and nonsusceptible individuals are useful. We consider such models for the analysis of bivariate interval-censored failure time data with a model for bivariate binary susceptibility indicators and a copula model for correlated failure times given joint susceptibility. We develop likelihood, composite likelihood, and estimating function methods for model fitting and inference, and assess asymptotic-relative efficiency and finite sample performance. Extensions dealing with higher-dimensional responses and current status data are also described. [ABSTRACT FROM AUTHOR]

Emotion regulation plays a crucial role in an individual's well-being, as it is known that deficits in regulating emotions can lead to psychological and psychiatric disorders. Cognitive reappraisal is widely considered to be an adaptive and effective emotion-regulation strategy. People are more or less able to apply it, but it is still not clear how reappraisal affects brain structures and the psychological profile of individuals. In our study we thus aimed to explore the impact of applying reappraisal at both the neural and the psychological level. Source-based morphometry (SBM), a whole-brain multivariate method based on the Independent Component Analysis that extracts patterns of covariation of gray matter ("independent networks"), was applied to the MRI images of 37 participants. In order to enrich their psychological profiles, we measured their experienced affectivity (PANAS) and their empathic abilities (IRI). Based on the frequency of applying reappraisal (ERQ), participants were divided into low and high reappraisers (18 vs. 19). An independent source of gray matter emerged as being different between the groups: specifically, low reappraisers showed more gray matter volume concentration in a network including the frontal, temporal, and parietal regions as compared to high reappraisers. At the psychological level, low reappraisers reported a more strongly experienced negative affect, while no difference among reappraisers emerged with regard to empathic abilities. Capitalizing on a multivariate method for structural analysis that is innovative in this field, this study extends previous observations on individual differences in the ability to regulate emotions, and it describes a plausible impact of reappraisal on brain structures and affectivity. [ABSTRACT FROM AUTHOR]