Development of Tendering Duration Models for Federal Government Building Projects in Nigeria

The Study sought to fundamentally generate information on the relationship between tendering duration and project complexity/determinants of cost and duration in order to determine via a scientific model, realistic tendering durations of various types of federal building projects in Nigeria.  To achieve this, historical data on project cost, project duration and tendering duration were obtained for 78 federal building projects.  They were subjected to correlation and regression analysis to test for statistical significance and the relationship between tendering duration and cost variables. Results of the regression analysis revealed that there is a significant relationship between tendering duration and project cost, which can be seen from the strong statistical correlation; R square value = 0.53 and the P(significant) values of 0.00 < alpha value ? (0.05)adopted in the study. Furthermore, a significant relationship between tendering duration and project duration was revealed. The statistical correlation was however observed to be higher; with R square value = 0.80 and P(sig) values < ? (0.05). Hence, the following models were developed which proved significant for the sampled projects based on the quadratic regression equation: First, the tendering duration – project cost model which is in the form; TD = 2.574 + 1.137E-9C – 5.245E-20C2, where TD is the tendering duration in weeks and C, project cost.  Second, the tendering duration-project duration model expressed as TD = 1.299 + 0.073D + 0.000D2, where D is the project duration in weeks. The study recommends that the Federal Government should formulate bidding deadlines for different categories of building projects based on complexity factors of cost and duration, via adoption of the models proposed by the study. This will promote standards, efficiency in project planning, achievement of fairness and transparency in the public tendering system. Key Words: Building Projects, Development, Federal Government, Models, Nigeria, Tendering Duration

Dataset of urban development analysis in a section of Kuje Area Council, Abuja, Nigeria.

Urban development will likely continue to increase in suburban areas to cater for the growing human population. In Nigeria, the relevant analysis of these urban developments is not well documented. This article presents spatiotemporal datasets for analysing urban developments in a suburb of Kuje, an Area Council within the Federal Capital Territory of Nigeria. Data from Google Earth (GE) historical imagery of 2005 was used as a baseline for analysis and was compared with a UAV digital orthomosaic of 2019 to quantify urban developments. This data provides useful information on the status of urban development that has taken place in the Kuje suburb over 14 years. The data will be of great importance to town planners and urban development authorities for future planning, and for making informed decisions about urban development issues in the area

Towards A Responsible Entrepreneurship Education and the Future of the Workforce

Highlights • PRME provides a Compass for universities to embed responsible education. • Limited information is available on the stream of African Entrepreneurship education. • Many universities are ill-equipped to develop adequate skills required for the modern job market. • This study is based on the Curricular, Co-curricular and Extra-curricular Learning Pipeline Model. Abstract This article explores how entrepreneurship education (EE) could be adopted towards improving graduate’s skills and preparing the future workforce. It adopts interviews with 30 experienced higher education academics, executives of employment and work placement agencies in Nigeria that reveals substantial benefits of adopting entrepreneurial pedagogics, critical thinking and problem-based learning (PBL). The critical question is how can EE practices be utilised in higher education to improve future workforce? Linked to the UN Principles of Responsible Management Education (PRME), this study is based on the model of curricular, co-curricular and extra-curricular learning pipeline that focuses on ‘learning in the curriculum’ and ‘learning beyond the curriculum’. The model somehow links to the six domains that formed our analytical model – knowledge and cognitive learning, innovation in teaching pedagogy, change in thinking, change in attitudes, social learning and change in action

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Explainable Artificial Intelligence (XAI) for Intrusion Detection and Mitigation in Intelligent Connected Vehicles: A Review

The potential for an intelligent transportation system (ITS) has been made possible by the growth of the Internet of things (IoT) and artificial intelligence (AI), resulting in the integration of IoT and ITS—known as the Internet of vehicles (IoV). To achieve the goal of automatic driving and efficient mobility, IoV is now combined with modern communication technologies (such as 5G) to achieve intelligent connected vehicles (ICVs). However, IoV is challenged with security risks in the following five (5) domains: ICV security, intelligent device security, service platform security, V2X communication security, and data security. Numerous AI models have been developed to mitigate the impact of intrusion threats on ICVs. On the other hand, the rise in explainable AI (XAI) results from the requirement to inject confidence, transparency, and repeatability into the development of AI for the security of ICV and to provide a safe ITS. As a result, the scope of this review covered the XAI models used in ICV intrusion detection systems (IDSs), their taxonomies, and outstanding research problems. The results of the study show that XAI though in its infancy of application to ICV, is a promising research direction in the quest for improving the network efficiency of ICVs. The paper further reveals that XAI increased transparency will foster its acceptability in the automobile industry