This study examines the state of maintenance of public hospitalbuildings in Southwest Nigeria, and in the process identifi es thesignifi cant difference(s) in the operational state of Federal andState-owned public hospitals within the study area. In achievingthe aim, the study adopts a survey technique with a total of552 questionnaires, comprising 206 sampled maintenancestaff and 346 users of public hospitals. The survey covers 46public hospitals representing 40% of the total number of publichospitals existing in Southwest Nigeria. The 46 public hospitalsconsist of all the 11 Federal-owned hospitals and 35 randomlyselected State-owned. Data collected are analysed using theKendall Coeffi cient of Concordance and Pearson Chisquare. Thefi ndings of the study reveal that the state of maintenance of publichospital buildings is good. While the structure/fabric and physicalconditions are rated highly, the services are poorly rated. Thisstudy, which hypothesises that there is no difference in the stateof maintenance, fi nds statistical difference in the performance ofthe services. It recommends that Federal and State governmentsaddress neglect in the services sector and plan their maintenanceprogrammes more effectively.

the failure of buildings is a national problem, which not only results in waste of materials, financial and human resources but, sometimes in loss of lives and properties. This occurrence has been a challenge to the professionals in the industry and as such necessitates the need for this study.The study examines the involvement of professionals in the incidence of buildings collapse in Lagos state. The research also identified and assessed the causes of building failure, evaluated the effects of such failures on construction professionals, the client, and the nation as a whole.In achieving these objectives, the study adopts a survey technique with a total of 65 questionnaires mainly for the professionals in the industry in which 50 were retrieved and used for the analysis. Random sampling technique was used in the selecting of the sample size. Data collected were analyzed using the frequency table, mean item score, one sample t test and paired sample t test. The study revealed that the building industry is full of quacks and inexperienced contractors. Their involvement in building construction has led to a lot of collapse in the past and at present. Poor management and leadership on the part of the Site Engineers and Builders have also contributed to many buildings failures. The analysis reveals that there are significant differences in the causes of building failure.The failures of buildings also have significant effects on the stakeholders.The study recommends that for all construction works being undertaken, such should be designed and supervised by qualified and registered Engineers and Builders. Also code of ethical conduct of building profession should be strongly enforced by the bodies concerned as to prevent ethical abuse by the professionals in the industry.

In recent years, there has been an increase in the number of applicants seeking admission into architecture programmes. As expected, prior academic performance (also referred to as pre-enrolment requirement) is a major factor considered during the process of selecting applicants. In the present study, machine learning models were used to predict academic success of architecture students based on information provided in prior academic performance. Two modeling techniques, namely K-nearest neighbour (k-NN) and linear discriminant analysis were applied in the study. It was found that K-nearest neighbour (k-NN) outperforms the linear discriminant analysis model in terms of accuracy. In addition, grades obtained in mathematics (at ordinary level examinations) had a significant impact on the academic success of undergraduate architecture students. This paper makes a modest contribution to the ongoing discussion on the relationship between prior academic performance and academic success of undergraduate students by evaluating this proposition. One of the issues that emerges from these findings is that prior academic performance can be used as a predictor of academic success in undergraduate architecture programmes. Overall, the developed k-NN model can serve as a valuable tool during the process of selecting new intakes into undergraduate architecture programmes in Nigeria.

Exposure of humans to high concentrations of PM2.5 has adverse effects on their health. Researchers estimate that exposure to particulate matter from fossil fuel emissions accounted for 18% of deaths in 2018—a challenge policymakers argue is being exacerbated by the increase in the number of extreme weather events and rapid urbanization as they tinker with strategies for reducing air pollutants. Drawing on a number of ensemble machine learning methods that have emerged as a result of advancements in data science, this study examines the effectiveness of using ensemble models for forecasting the concentrations of air pollutants, using PM2.5 as a representative case. A comprehensive evaluation of the ensemble methods was carried out by comparing their predictive performance with that of other standalone algorithms. The findings suggest that hybrid models provide useful tools for PM2.5 concentration forecasting. The developed models show that machine learning models are efficient in predicting air particulate concentrations, and can be used for air pollution forecasting. This study also provides insights into how climatic factors influence the concentrations of pollutants found in the air.

In the face of the deplorable state of buildings across the various tertiary institutions in