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The uncontrolled dumping of agricultural and industrial wastes and waste water has serious environmental and public health consequences. In order to utilise these wastes for agricultural purposes, the response of soybean seed germination to the desiccated coconut water and dairy whey was examined. In this investigation the nutritional content analysis and enumeration of microorganisms found in desiccated coconut water and dairy whey obtained, respectively from coconut mills and dairy were carried out. Different microorganisms were isolated and characterized morphologically and biochemically characterized. The results showed that the desiccated coconut water included a diverse microbial population such as bacteria (286.4 × 104 cfu mL-1), yeast (8.89 × 104 cfu mL-1), actinomycetes (1.00 × 104 cfu mL-1), free living nitrogen fixers (26.7 × 104 cfu mL-1), phosphate solubilizing bacteria (33.3 × 104 cfu mL-1), Pseudomonas sp. (73.3 × 104 cfu mL-1) and coliforms (76.7 × 104 cfu mL-1). Whereas, in dairy whey, microorganisms such as bacteria (153.3 × 104 cfu mL-1), yeasts (3.30 × 104 cfu mL-1), actinomycetes (0.12 × 104 cfu mL-1) and coliforms (56.7 × 104 cfu mL-1) were found. The effect of desiccated coconut water and dairy whey on soybean seed germination was studied by pre-treating the seeds and examining different seed quality indicators. Compared to all other treatments and the control, seeds pre-treated with 10 per cent desiccated coconut water and 10 per cent whey had significantly higher germination percentage (98.00 and 97.33), shoot length (13.58 cm and 14.30 cm), root length (21.94 cm and 20.12 cm), seedling dry weight (404.28 mg and 100.19 mg), seedling vigour index-I (3481 and 3351) and seedling vigour index -II (39619 and 39019), respectively. [ABSTRACT FROM AUTHOR]

Analysis of matrix variate data is becoming increasingly common in the literature, particularly in the field of clustering and classification. It is well known that real data, including real matrix variate data, often exhibit high levels of asymmetry. To address this issue, one common approach is to introduce a tail or skewness parameter to a symmetric distribution. In this regard, we introduce here a new distribution called the matrix variate skew-t distribution (MVST), which provides flexibility, in terms of heavy tail and skewness. We then conduct a thorough investigation of various characterizations and probabilistic properties of the MVST distribution. We also explore extensions of this distribution to a finite mixture model. To estimate the parameters of the MVST distribution, we develop an EM-type algorithm that computes maximum likelihood (ML) estimates of the model parameters. To validate the effectiveness and usefulness of the developed models and associated methods, we performed empirical experiments, using simulated data as well as three real data examples, including an application in skin cancer detection. Our results demonstrate the efficacy of the developed approach in handling asymmetric matrix variate data.

One-shot devices are products that can only be used once. Typical one-shot devices include airbags, fire extinguishers, inflatable life vests, ammo, and handheld flares. Most of them are life-saving products and should be highly reliable in an emergency. Quality control of those productions and predicting their reliabilities over time is critically important. To assess the reliability of the products, manufacturers usually test them in controlled conditions rather than user conditions. We may rely on public datasets that reflect their reliability in actual use, but the datasets often come with missing observations. The experimenter may lose information on covariate readings due to human errors. Traditional missing-data-handling methods may not work well in handling one-shot device data as they only contain their survival statuses. In this research, we propose Multiple Imputation with Unsupervised Learning (MIUL) to impute the missing data using Hierarchical Clustering, k-prototype, and density-based spatial clustering of applications with noise (DBSCAN). Our simulation study shows that MIUL algorithms have superior performance. We also illustrate the method using datasets from the Crash Report Sampling System (CRSS) of the National Highway Traffic Safety Administration (NHTSA).

In this paper, we have considered systems with dependent components having a joint distribution modeled by an Archimedean copula and with component lifetimes following accelerated failure time and modified proportional hazards distributions. We have then established characterization results specifically for series, fail-safe, 2-out-of n and parallel systems through comparisons with average systems in terms of mean residual life, hazard rate and reversed hazard rate orders. We have also discussed various stochastic orderings and ageing results for the residual lives of parallel and series systems. The results established here are quite general, and several examples have been used to illustrate all the results and their reliability implications.

Al–Li alloy has been widely used in the aerospace field owing to its high strength and low density. In this study, alternating current cold metal transfer (AC CMT) along with a high-frequency pulse current technique was used to weld a 2060 Al–Li alloy using an ER5356 wire. The effect of pulse frequency on the arc shape, microstructure, and mechanical properties of the welded joints was examined, and mechanical performance testing was conducted. The results revealed that the arc diameter, arc length, and arc volume showed a trend of increasing first and then decreasing with an increase in the pulse frequency and reached their peak values when the pulse frequency was 50 kHz. Coupling the welding process with a high-frequency pulse resulted in grain refinement, which was attributed to the stirring action of the arc force. Both the porosity levels and grain size decreased with increasing frequency. When the pulse frequency was 70 kHz, the porosity level was the lowest, and the grain size was refined to 24.1 μm. The tensile strength of the welded joints also increased with the pulse frequency, and a maximum tensile strength of 249 MPa was observed at 70 kHz.

In this paper, we consider parallel-series and series-parallel systems comprising dependent components that are drawn from a heterogeneous population consisting of m different subpopulations, and each subsystem is equipped with a starter device. We also make the assumption that the components within each subpopulation are dependent, while the subsystems themselves are independent. The joint distribution of these subsystems is modeled using an Archimedean copula. Our research considers a general setting in which each subpopulation has a different Archimedean copula for its dependence. By adopting this general setup, we investigate the stochastic, hazard rate, and reversed hazard rate orders between these systems. Furthermore, we provide several numerical examples to demonstrate all the theoretical results established in this study. These results broaden the scope of the known results in the existing literature.

Multivariate count data are often modeled via a multivariate Poisson distribution, but it contains an underlying, constraining assumption of data equi-dispersion (where its variance equals its mean). Real data are oftentimes over-dispersed and, as such, consider various advancements of a negative binomial structure. While data over-dispersion is more prevalent than under-dispersion in real data, however, examples containing under-dispersed data are surfacing with greater frequency. Thus, there is a demonstrated need for a flexible model that can accommodate both data types. We develop a multivariate Conway–Maxwell–Poisson (MCMP) distribution to serve as a flexible alternative for correlated count data that contain data dispersion. This structure contains the multivariate Poisson, multivariate geometric, and the multivariate Bernoulli distributions as special cases, and serves as a bridge distribution across these three classical models to address other levels of over- or under-dispersion. In this work, we not only derive the distributional form and statistical properties of this model, but we further address parameter estimation, establish informative hypothesis tests to detect statistically significant data dispersion and aid in model parsimony, and illustrate the distribution’s flexibility through several simulated and real-world data examples. These examples demonstrate that the MCMP distribution performs on par with the multivariate negative binomial distribution for over-dispersed data, and proves particularly beneficial in effectively representing under-dispersed data. Thus, the MCMP distribution offers an effective, unifying framework for modeling over- or under-dispersed multivariate correlated count data that do not necessarily adhere to Poisson assumptions.

Solar power has become a key green source of energy. An important factor that affects the reliability and lifetime of solar modules is the quality of encapsulation through the lamination process, which melts the ethylene vinyl acetate (EVA) to make the solar cells combine with the front glass side and the rear side units. The degree of crosslinking or curing degree for EVA sheets, when the EVA sheet gets heated, can affect the efficiency of the performance and power conversion of solar modules. For this reason, motivated by a lamination data, we construct here a statistical model for describing the relationship between the curing degree and the lamination factors (temperature and time). Then, based on some specification limits on the curing degree, the optimal lamination time for solar modules can be determined at different temperatures. Moreover, the optimal sample size allocation in a test for measuring EVA sheets can also be determined. A simulation study is finally carried out to show the closeness of simulation results to the asymptotic results.

The hypergeometric distribution has gained its importance in practice as it pertains to sampling without replacement from a finite population. It has been used to estimate the population size of rare species in ecology, discrete failure rate in reliability, fraction defective in quality control, and the number of initial faults present in software coding. Recently, Borges et al. considered a COM type generalization of the binomial distribution, called COM–Poisson–Binomial (CMPB) and investigated many of its characteristics and some interesting applications. In the same spirit, we develop here a generalization of the hypergeometric distribution, called the COM–hypergeometric distribution. We discuss many of its characteristics such as the limiting forms, the over- and underdispersion, and the behavior of its failure rate. We write its probability-generating function (pgf) in the form of Kemp’s family of distributions when the newly introduced shape parameter is a positive integer. In this form, closed-form expressions are derived for its mean and variance. Finally, we develop statistical inference procedures for the model parameters and illustrate the results by extensive Monte Carlo simulations.