A better approach to discuss medical science and engineering data with a modified Lehmann Type – II model

Background: Modeling with the complex random phenomena that are frequently observed in reliability engineering, hydrology, ecology, medical science, and agricultural sciences was once thought to be an enigma. Scientists and practitioners agree that an appropriate but simple model is the best choice for this investigation. To address these issues, scientists have previously discussed a variety of bounded and unbounded, simple to complex lifetime models. Methods: We discussed a modified Lehmann type II (ML-II) model as a better approach to modeling bathtub-shaped and asymmetric random phenomena. A number of complementary mathematical and reliability measures were developed and discussed. Furthermore, explicit expressions for the moments, quantile function, and order statistics were developed. Then, we discussed the various shapes of the density and reliability functions over various model parameter choices. The maximum likelihood estimation (MLE) method was used to estimate the unknown model parameters, and a simulation study was carried out to evaluate the MLEs' asymptotic behavior. Results: We demonstrated ML- II's dominance over well-known competitors by modeling anxiety in women and electronic data.

The Marshall-Olkin Half Logistic-G Family of Distributions With Applications

Attempts have been made to define new classes of distributions that provide more flexibility for modeling data that is skewed in nature. In this work, we propose a new family of distributions namely the Marshall-Olkin Half Logistic-G (MO-HL-G) based on the generator pioneered by [Marshall and Olkin , 1997]. This new family of distributions allows for a flexible fit to real data from several fields, such as engineering, hydrology, and survival analysis. The structural properties of these distributions are studied and its model parameters are obtained through the maximum likelihood method. We finally demonstrate the effectiveness of these models via simulation experiments.

The Marshall-Olkin Half Logistic-G Family of Distributions With Applications

Attempts have been made to define new classes of distributions that provide more flexibility for modeling data that is skewed in nature. In this work, we propose a new family of distributions namely the Marshall-Olkin Half Logistic-G (MO-HL-G) based on the generator pioneered by [Marshall and Olkin , 1997]. This new family of distributions allows for a flexible fit to real data from several fields, such as engineering, hydrology, and survival analysis. The structural properties of these distributions are studied and its model parameters are obtained through the maximum likelihood method. We finally demonstrate the effectiveness of these models via simulation experiments.

Soil resistance in the process of dams’ irrigation canals profiling

The questions of processing technology of dam channels using appropriate mechanisms are presented taking into account the requirements of modern engineering hydrology. Channel profiling with the best quality and performance can be carried out by means of mechanization at the expense of working tools, able to process not only longitudinal but also cross-sections of channels. According to calculations, there was established, that the best condition for moving soil on the channel slope in front of the passive knife, is an equable distribution along the slope. It was marked, that the strength of the soil pressure is most affected by the installation value of the trowel knife and the thickness of the loosened soil layer to be compacted. Recommendations on increasing the active working body stability of the dam profiler are given.

Results of probabilistic hydrological processes research at RSHU

Currently, long-term estimates can be obtained either under the assumption of statistical stationarity of hydrometeorological processes using actual series of observations for the previous decades, i.e., in fact, by extrapolating “frozen” current probabilistic estimates to the future, or by modeling (calculation) based on equilibrium climatic scenarios under the assumption of statistical sustainability of runoff series, according to which parameterization of forecast models of runoff formation is conducted. The article considers the methodology of partially infinite hydrology, which includes sustainable forecasting of runoff and diagnostics of bifurcations of its formation, allows solving fundamentally new hydrological problems (including problems of engineering hydrology) related to the possibility of obtaining longterm estimates of probabilistic characteristics of long-term river runoff under the conditions of evolutionary changes in the runoff formation factors (climate and anthropogenic activity in catchment areas). Using the methods and patterns of partially infinite hydrology and relying only on the available hydrometeorological information (obtained at the state network of standard observations), known climatic scenarios and plans for the socio-economic development of the territory, the following main results have been obtained: 1) river basins have been diagnosed (as well as time intervals in the future), the ones in which (and when) it is possible to change the additive mechanism of the smooth evolution of the flow formation process to a bifurcation mechanism (the appearance of bifurcation foci) being identified, i.e. engineering hydrology documents can be questioned; 2) a methodology has been developed for sustainable forecasting of the probabilistic characteristics of long-term river runoff using various options for its formation models (unimodal, polymodal, one-dimensional, multidimensional, etc.).

Industry-Practice-Based Engineering Hydrology Education at USQ, Australia

Engineering education must embrace several challenges, such as increased numbers of work-based students, increased demand for online education, mismatches in employability skills and industry requirements, and lack of student engagement. The hydrology course at the University of Southern Queensland attracts more than 100 students every year, where more than 70% of students are off-campus and most of them are work-based. This study explored how an online hydrology course can embrace industry practice and engage students in order to achieve learning outcomes. Industrial careers in hydrology involve extensive use of hydroclimatological data and modeling applications. The course modules, learning objectives and outcomes, and assessment tools have been designed to align with industry practices. Active participation of students was observed in self-assessment quizzes and discussion forums. The course was rated very well in achieving learning outcomes and in overall student satisfaction. Students appreciated the well-structured real-world and professional practice in the course.