We aim at tackling the problem of inadequate specification of a Markov manpower model in this paper, by formulating a procedure for validating the inclusion or non-inclusion of some transition parameters in the model. The mover-stayer principle and its extensions are employed to incorporate hidden classes in the model to achieve more homogeneity and this is compared with the model without the hidden classes, which is more parsimonious, using Likelihood ratio statistic, Akaike Information Criterion and Bayesian Information Criterion. The illustration shows a case of manpower data where, up to a certain level of hidden states, homogeneity is more important than parsimony.

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The importance of this research to the literature lies in the ability to develop a hybrid method of Topological Data Analysis and Unsupervised Machine Learning (TDA-uML) for flood detection. The working method in TDA-uML entails collection and loading of datasets, feature representation, data preprocessing (i.e., training, testing, computation, and validation), and data classification. Three properties make TDA distinct from traditional methods: coordinate-invariance, deformative-invariance, and compressed-representative. Formerly utilized hydrologic, hydraulic, and statistical models for flood control were frequently erroneous in their forecasts, lacked the use of hybrid models, and were not validated. The main research objective is to develop a hybrid method for predicting floods. The motivation is to fill research gaps by using TDA-uML methods for persistent homology (PH) and synthetic k-means clustering. The results will be used to compare and categorize the features that are produced. Seven states were selected based on Nigeria's flood history and affected population. The 7 states are located within the 8 hydrological areas of Nigeria. The efficiency of the resultant validity was 91%. The findings contributed to the development of a model for flood prediction and management; topological features were extracted from the data to predict and categorize the risk.

This paper examined the knowledge, compliance and impact of hand hygiene among healthcare professionals during COVID-19 outbreak in South-East, Nigeria. The data used in this study were collected from twenty (20) hospitals in South-East, Nigeria using questionnaire with closed-type question forms. A total number of 600 questionnaires were used in this study. Two-way CATANOVA was used to examine the gender and health profession that have well knowledge, compliance and impact experience of hand hygiene during COVID-19 outbreak. The result showed no statistically significant difference in the knowledge, compliance and impact experience of hand hygiene among four major health professions (medical doctors, nurses, pharmacists, laboratory scientists) and also between the genders at a 5% significance level. The findings showed that the changing of healthcare professional from one health profession to another does not affect the knowledge, compliance and impact experience of hand hygiene. It was noticed that 599(99.8%) healthcare professionals have good knowledge of hand hygiene, 395(65.8%) practice hand hygiene every time, and 507(84.5%) have high impact experience of hand hygiene. There is enhancement in the knowledge, compliance and impact experience of hand hygiene of healthcare professionals as their years of service increase.