Journal of Biometrics & Biostatistics

In this paper, the social factors affecting the level of acceptance of covid-19 vaccine in Akanu Ibiam federal polytechnic Unwana was studied. A stratified random sampling was employed to draw samples of staff and students of the polytechnic. The schools were used as strata to make the sample representative. A structured questionnaire was administered to the sample points, their responses were analyzed using a percentages, cross-tabulation chi square test and probit regression model using the following variables: age, sex, religion, tribe, education status and work status to ascertain if any or all of the factors have influence on the level of acceptance of the covid-19 vaccine. From the result obtained, that the acceptance level of Covid-19 vaccine by the respondents is 47.89% a little below average, and the goodness of fit test =219.568 at 5 percent level of significance showed that age, education and work status of respondents influenced the acceptance level of covid-19 positively, which implies that they increase the acceptance level covid-19 vaccine. While sex, religion and tribe affect acceptance of covid-19 vaccine negatively, that is, decreases its acceptance level in the study area. It is therefore recommended that scarcity of the vaccine should be addressed, to make it easily accessible to citizens even to the remote and rural areas.

Quality monitoring in manufacturing processes, particularly in welding, plays a pivotal role in ensuring product integrity and consistency. This paper explores the significance of data characteristics in welding quality monitoring, emphasizing the diverse types of data generated, their properties, and their implications for effective monitoring strategies. By analyzing various data sources, including sensor data, image data, and historical records, this paper aims to provide insights into the challenges and opportunities associated with leveraging these data types for enhancing welding process quality. Additionally, it discusses the role of advanced analytics techniques, such as machine learning and artificial intelligence, in harnessing the potential of these data for real-time monitoring and predictive maintenance. Through a comprehensive understanding of data characteristics, manufacturers can optimize their welding processes, minimize defects, and improve overall product quality.

Monitoring the structural health of engineering systems is crucial for ensuring their safety and reliability. However, the accuracy of structural health monitoring (SHM) data can be compromised by environmental factors, such as temperature fluctuations, which may mask or distort the actual structural responses. This paper introduces an innovative approach using an Adaptive Bandwidth Filter Algorithm to separate structure responses from monitoring data, particularly focusing on mitigating the impact of temperature effects. The algorithm's adaptability allows for dynamic adjustments, enhancing its effectiveness across a range of environmental conditions. This research addresses the complexities of temperature-induced distortions in structural monitoring data and proposes a solution for improved accuracy in assessing structural health.

This meta-analysis explores the hygrothermal optimization strategies employed in the reuse of excavated soil in construction across diverse climate zones. The study synthesizes findings from global literature, examining the effectiveness of various techniques in mitigating the impact of climate on buildings constructed using excavated soil. The analysis spans regions with distinct climatic conditions, providing insights into the adaptability and performance of hygrothermal optimization methods. The construction industry is increasingly focusing on sustainable practices and one avenue gaining attention is the reuse of excavated soil in building materials. This meta-analysis aims to provide a comprehensive overview of hygrothermal optimization strategies employed in constructions across different climate zones, emphasizing the role of excavated soil reuse.

In clinical research, a medical predictive model is intended to provide insight into the impact of risk factors (predictors) such as demographics and patient characteristics on clinical outcomes. A validated medical predictive model informs disease status and treatment effects under study. More importantly, it can be used for disease management. However, a gap in the development process of these models is often observed. That is, most studies only focus on the internal validation for the model's reproducibility but overlook the external validation needed for evaluating generalizability. To solve this issue, this article proposes several methods for assessing both the reproducibility and generalizability of a developed/ validated medical predictive model. The generalizability estimation approaches allow for sensitivity analysis in situations where data on new populations is not available, which provides valuable insights into the model's applicability to patients from a different population.