General classes of bivariate distributions for modeling data with common observations

On the bivariate normal distribution and association models for ordinal categorical data

Relatively simple approaches to estimating Weibull parameters for a general class of devices are developed through regression models. It is assumed that data are collected on a number of device types belonging to a general class. For each device type, the only information available is the number of devices being observed, the total time observed and the total number of failures. By assuming a constant shape parameter and a scale parameter that may vary with the characteristics of the device-type, the least squares method is used to provide estimates of the parameters of a two-parameter Weibull distribution for both replacement and nonreplacement data. An approach is also suggested for dealing with troublesome cases of zero failure occurrences. A numerical example is provided to illustrate the approach.

The development of occupational safety and health science in the work environment can be seen with the development of advanced science and technology as well as the emergence of new innovations in the work environment and encouraging companies to improve the knowledge and skills of their human resources. The purpose of this study is to determine the Influence of Occupational Safety and Health Education (K3) on the Behavior of Using Personal Protective Equipment at the Special UPTD of Haji Medan Hospital. This research includes quasi-experimental research. This study uses a one group pretest and pottest design. The population in this study is emergency room nurses at the Special UPTD of Haji Medan Hospital which totals 50 nurses in June-August 2024. The sampling technique in this study is total sampling. The measuring tool in this study uses a univariate and bivariate distribution of non-parametric data with the Wilcoxon test. The results of the Wilcoxon statistical test of both knowledge and attitude obtained a P-value of 0.000. This means that at alpha 5% there is a significant difference in median score between before and after counseling. Meanwhile, in the aspect of action, there was an increase in the number of PPE users between before and after counseling. Because the results of Asymp.Sig of knowledge, attitudes and actions are less than 0.05, H0 is rejected so that there is an influence of occupational health and safety counseling (K3) on the behavior of using PPE. In conclusion, there is an influence of Occupational Safety and Health Counseling (K3) on the behavior of using personal protective equipment at the Special UPTD of Haji Medan Hospital. The suggestions are expected to increase the frequency of counseling and provide information on the importance of using personal protective equipment in the workplace, provide strict sanctions against nurses who do not use personal protective equipment at work, increase the frequency.

In this paper we describe a new class of discrete multivariate distributions which verify that their probability mass function is invariant when their univariate variables are permuted. These distributions may be generated by a multivariate extension of the Gauss function 2F1 with matrix argument. A methodology that permits the fit of these distributions to real data is developed. A fit of a distribution for bivariate real data is shown and is compared with fits obtained by means of other usual bivariate distributions generated by extensions of the Gauss function.

В статті розглянуто задачу прогнозування дискретних значень потужності споживання електричної енергії, яка вирішується з використанням одного з методів штучних нейронних мереж - обмеженої машини Больцмана. В цьому методі прогнозування здійснюється шляхом підлаштування вагових функцій шарів нейронної мережі таким чином, щоб мінімізувати відстань між реальними та розрахунковими значеннями. У кожній фазі навчання мережі за допомогою відповідних математичних функцій розраховуються і змінюються коефіцієнти ваг з урахуванням їх поточних значень. В результаті після навчання система здатна ефективно прогнозувати часові залежності потужності споживання електричної енергії, що є важливим при розробці системі керування електроспоживанням промислових та побутових об’єктів.

On construction of general classes of bivariate distributions

In this research, we propose deep networks that discover Granger causes from multivariate temporal data generated in financial markets. We introduce a Deep Neural Network (DNN) and a Recurrent Neural Network (RNN) that discover Granger-causal features for bivariate regression on bivariate time series data distributions. These features are subsequently used to discover Granger-causal graphs for multivariate regression on multivariate time series data distributions. Our supervised feature learning process in proposed deep regression networks has favourable F-tests for feature selection and t-tests for model comparisons. The experiments, minimizing root mean squared errors in the regression analysis on real stock market data obtained from Yahoo Finance, demonstrate that our causal features significantly improve the existing deep learning regression models.

Probabilistic assessment of potential leachate leakage from livestock mortality burial pits: A supervised classification approach using a Gaussian mixture model (GMM) fitted to a groundwater quality monitoring dataset

Deep learning has been widely applied to intelligent fault diagnosis. However, most existing methods are trained offline in static settings and struggle to handle dynamic, open environments with evolving data distributions. In actual industrial applications, changing operating conditions produce non-independent and identically distributed (non-IID) data streams, while access to target domain data is often unavailable, limiting the effectiveness of conventional transfer learning methods. To address these issues, this paper proposes a domain generalization class incremental transfer learning network (DGCITL). A novel loss function combining CORAL and subspace robust embedding (SSRE) is introduced to align features across domains without target data. A domain generalization incremental learning strategy is then designed to preserve previous knowledge while accommodating new classes, leveraging information aggregation and dynamic adaptation. Instead of traditional sample replay, DGCITL employs a Graph Replay Mechanism using graph convolution to capture and reuse learned knowledge, effectively mitigating catastrophic forgetting. Experimental results show that the proposed method performs well in the incremental task of variable operating condition class, providing an efficient solution for cross-domain intelligent fault diagnosis without target domain.

In this paper, a new class of bivariate distributions called the bivariate Gumbel-G family is proposed, whose marginal distributions are Gumbel-G families. Several of its statistical properties are derived. After introducing the general class, a special model of the new family is discussed in-detail. Bayesian and maximum likelihood techniques are used to estimate the model parameters. Simulation study is carried out to examine the bias and mean square error of Bayesian and maximum likelihood estimators. Finally, a real data set is analyzed for illustrative the flexibility of the proposed bivariate family.