Dr Sandra Obiora

Outbreaks, Epidemics, and Health Security: Ensuring Future Preparedness for Small Island Nations and the World reviews the many lessons learned from the COVID-19 pandemic.

The onset of the COVID-19 pandemic has changed consumer usage behavior towards mobile payment (m-payment) services. Consumer usage behavior towards m-payment services continues to increase due to access to usage experiences shared through online consumer reviews (OCRs). The proliferation of massive OCRs, coupled with quick and effective decisions concerning the evaluation and selection of m-payment services, is a practical issue for research. This paper develops a novel decision evaluation model that integrates OCRs and multi-attribute decision-making (MADM) with probabilistic linguistic information to identify m-payment usage attributes and utilize these attributes to evaluate and rank m-payment services. First and foremost, the attributes of m-payment usage discussed by consumers in OCRs are extracted using the Latent Dirichlet Allocation (LDA) topic modeling approach. These key attributes are used as the evaluation scales in the MADM. Based on an unsupervised sentiment algorithm, the sentiment scores of the text reviews regarding the attributes are calculated. We convert the sentiment scores into probabilistic linguistic elements based on the probabilistic linguistic term set (PLTS) theory and statistical analysis. Furthermore, we construct a novel technique known as probabilistic linguistic indifference threshold-based attribute ratio analysis (PL-ITARA) to discover the weight importance of the usage attributes. Subsequently, the positive and negative ideal-based PL-ELECTRE I methodology is proposed to evaluate and rank m-payment services. Finally, a case study on selecting appropriate m-payment services in Ghana is examined to authenticate the validity and applicability of our proposed decision evaluation methodology.

Unfortunately, within the framework of blockchain contracting, a significant gap exists in comprehending contractual behavior, and the feasibility of predictive contracts has largely remained unexplored. A principal obstacle stems from the absence of a seamless integration between predictive concepts and blockchain technology. This deficiency is attributed to a failure to consider the inherent characteristics of blockchain when developing solutions aimed at improving predictive capabilities within blockchain-based systems. Many existing predictive approaches function externally to the fundamental blockchain framework, rendering them impractical. This has caused the idea of predictive contracts to be seen as unfeasible due to the character of blockchain smart contracts making it hard to do so. This includes its immutability and the inability for changes to be made once deployed. In this research, we introduce the concept of blockchain-based predictive contracting which stems from the theoretical idea of predictive contracting, and substantiate the feasibility of our approach, enabling blockchain smart contracts to adapt to changes in external environments upon which they depend. We attempt to achieve and prove the first phase of this idea, which we term “recalibration”. Here we provide a means for deployed smart contracts to become structurally changeable while responding to external situations without compromising their security. This we believe is the first phase needed for blockchain smart contracts before they can become predictable. Our approach capitalizes on the key-pair structure scheme utilized in existing blockchain systems to create a data signature, facilitating the identification of new smart contracts. We establish rules encompassing a configuration mechanism, empowering smart contracts to recognize newly-introduced agreements. Additionally, we implement an encoding system to enable the blockchain to respond to dynamic data. This we believe will provide a means for blockchain to be used well in industrial applications such as supply aircraft delivery networks and supply chain networks. To anticipate future scenarios, we devise a multi-versioning system that allows smart contracts to evolve over time. Our innovative concept is also demonstrated within a blockchain-based smart contract prediction scheme, ensuring the adaptability of blockchain-based smart contracts. This scheme comprises a smart contract tracing mechanism, an effective smart contract transitioning procedure, and a protocol for generating new smart contracting terms and conditions while preserving inherent trust within the system. Through extensive experimentation, involving opcode and smart contract ID extraction, Solidity Word2Vec model development, a blockchain-based embedding process, and smart contract versioning detection, we introduce the concept of blockchain-based predictive smart contracts. Notably, we observe a significant enhancement as multiple parties engage in complex operations on the blockchain, with an average gas cost of 31374215 Wei for demonstrating smart contractual operations within exogenous conditions. This validates the cost efficiency of our approach over prior methods. Our empirical results affirm the novelty and efficacy of our proposed concept.

Recently, emerging, developed, and developing economies have placed great emphasis on the need to attain environmental sustainability while achieving economic expansion. In an effort to offer possible policy options toward the attainment of sustainable development, this study examines the effect of economic growth on carbon emissions mitigation. Yearly panel data for 44 countries comprised of emerging, developed, and developing economies from 1990 to 2017 is used. To address the gap in the literature, this nexus is examined on seven layers of carbon emissions. This study reveals reliable and robust empirical findings with the use of system and difference general method of moments, random and fixed effects using the Durbin-Wu-Hausman test model, and feasible general least-squares estimation approaches. Our findings indicate that for developed economies, carbon emissions by the power industry have been mitigated and increased domestic credit to the private sector leads to a decrease in all layers of carbon emissions. Nevertheless, gross national income increase negatively impacts emissions by the transport sector. In emerging and developing economies, increased domestic credit to the private sector increases emissions by the power industry, transport sector, buildings, other combustion industries, and other non-major sectors. For all economies, an increase in domestic savings leads to an increase in all layers of carbon emissions. Compared with prior studies that simply focus on gross domestic product and total carbon emissions, our study provides detailed insights on the carbon emissions mitigation efforts by sector and economic group given the true drivers of economic expansion.

The urgent need to mitigate the severe environmental impacts of climate change necessitates a transition to a low-carbon energy infrastructure, crucial for decarbonization and achieving global sustainability goals. This study investigates the decarbonization trajectories of five major economies and significant carbon emitters: the United States of America (USA), China, Japan, Germany, and India. We focus on evaluating two decarbonization scenarios for power generation. Scenario 1 explores the use of a generic storage system for reducing critical excess electricity production (CEEP), maintaining the same thermal power plant capacity as in the reference year 2021. In contrast, Scenario 2 models thermal power plants to meet the exact electricity demand without introducing a new electricity storage system. The primary aim is to assess the feasibility and implications of achieving a 100% share of renewable and nuclear energy by 2030 and 2050 in these countries. EnergyPLAN software was utilized to model and simulate the electricity systems of these countries. The two scenarios represent different degrees of renewable energy integration, demonstrating possible transitional pathways towards an environmentally friendly electricity generation system. The study provides a comparative analysis of the outcomes for each country, focusing on carbon emissions reduction and the impact on annual total costs in 2030 and 2050. Results show that by 2030, China could reduce its emissions by 88.5% and 85.14% in Scenarios 1 and 2, relative to 2021 levels. From the two scenarios considered in all the countries, India records the highest percentage reduction while Germany has the least percentage emission in reference to 2021, with a potential decrease of 90.63% and 52.42% respectively. By 2050, carbon emissions in the USA will be reduced by 83% and 79.8% using Scenario 1 and Scenario 2 decarbonization pathways. This research significantly contributes to understanding the decarbonization potential of global electricity generation. It provides vital data for policymakers, energy planners, and stakeholders involved in developing sustainable energy policies.

In this study, the impact of the electricity crisis on the economic growth of Nigeria is presented. Unlike other existing studies that checked the effect of electricity consumption on economic development or environmental sustainability for different countries, the present study will further present a techno-economic analysis of a proffered solution to the imminent electricity crisis. Time-series regression models are used to analyze the effect of electricity consumption on economic development and environmental sustainability while RETScreen professional software is used to perform a techno-economic analysis and determine the feasibility of a 500-kW microgrid Solar Photovoltaic (PV) system integrated for electricity generation. From the analysis results, a strong positive correlation effect is evident between electricity consumption and GNI, as well as a strong negative correlation between electricity consumption and gross domestic savings. Also, strong positive correlation effects are evident in the case of carbon emissions by buildings, by the power industry, and by other combustion industries on electricity consumption in Nigeria. Considering the net present value, internal rate of return and payback periods, the use of solar PV systems for electricity generation is feasible in the 12 different locations in Nigeria studied in this research. The most feasible area for solar PV installation is the northern part of Nigeria as Gombe and Kaduna recorded a simple PBP and an equity PBP are 6.3 years and 7.4 years respectively.

Aristotle's Rhetorical Triangle, consisting of ethos, pathos, and logos, has been widely applied in various fields such as public speaking and writing. It underscores the necessity of integrating credibility, emotion, and logic in persuasive communication. This principle extends to leadership, where effective leaders employ these elements to inspire and guide followers. By blending credibility, emotional appeal, and logical reasoning, leaders can communicate their vision and facilitate transformation. In modern leadership, utilising ethos builds trust and authority, pathos connects with followers emotionally, and logos ensures decisions and communications are grounded in reason, resulting in more effective and impactful leadership strategies.

Computed Tomography has become a vital screening method for the detection of coronavirus 2019 (COVID-19). With the high mortality rate and overload for domain experts, radiologists, and clinicians, there is a need for the application of a computerized diagnostic technique. To this effect, we have taken into consideration improving the performance of COVID-19 identification by tackling the issue of low quality and resolution of computed tomography images by introducing our method. We have reported about a technique named the modified enhanced super resolution generative adversarial network for a better high resolution of computed tomography images. Furthermore, in contrast to the fashion of increasing network depth and complexity to beef up imaging performance, we incorporated a Siamese capsule network that extracts distinct features for COVID-19 identification.The qualitative and quantitative results establish that the proposed model is effective, accurate, and robust for COVID-19 screening. We demonstrate the proposed model for COVID-19 identification on a publicly available dataset COVID-CT, which contains 349 COVID-19 and 463 non-COVID-19 computed tomography images. The proposed method achieves an accuracy of 97.92%, sensitivity of 98.85%, specificity of 97.21%, AUC of 98.03%, precision of 98.44%, and F1 score of 97.52%. Our approach obtained state-of-the-art performance, according to experimental results, which is helpful for COVID-19 screening. This new conceptual framework is proposed to play an influential task in the issue facing COVID-19 and related ailments, with the availability of few datasets.

As the imperative to address climate change becomes more pressing, there is an increasing focus on limiting global temperature increase to 1.5 °C by the end of the century relative to pre-industrial levels. During the recent Conference of Parties (COP28), nations committed to tripling renewable energy generation to a minimum of 11,000 GW by 2030 and increasing the global annual energy efficiency from 2 % to 4 % annually until 2030. Additionally, the Food and Agricultural Organization (FAO) introduced a roadmap to transition the Agri-food system from a net emitter to a carbon sink. The role of carbon dioxide removal (CDR) is important; first to accelerate the near-term reduction in net emissions, counterbalance residual emissions at the point of net-zero by mid-century, and sustain large net negative emissions beyond mid-century to return warming to safe levels after decades of temporal overshoot. This paper assesses the impact of the COP 28 agreements, alongside the complementary role of CDR on emission levels, energy structure, land use, and global warming temperature. The findings indicate that implementing the COP28 pledges and FAO roadmap leads to a warming temperature of 2 °C, falling short of the ambitious 1.5 °C temperature limit. Likewise, more stringent actions on transitioning away from fossil plants is a high-priority mitigation action which drives significant emissions reduction. The modelled result shows that Agricultural soil carbon and biochar contribute 47–58 % share of the total CDR deployed in the stylized scenarios. In conclusion, CDR can expedite climate goals but must complement emission reduction efforts; hence, the transition away from fossil fuels should prompt the development of detailed roadmaps. Also, more global efforts should be placed on nature-based CDR methods, as they offer diverse co-benefits.

In this study, the use of artificial neural network (ANN) models for solar irradiance and solar PV parameters forecasting in Nigeria is presented. Although solar irradiance prediction exists in literatures, the use of ANN for solar PV parameter prediction has not been considered in previous studies. Six different locations are selected in Nigeria and data from these locations have been used to train/test the ANN models developed. This study aims to model ANN algorithms that can forecast solar irradiance and solar PV parameters based on an hourly time-step more accurately. A deep learning regression model built on Levenberg-Marquardt back propagation algorithm is used to train and test the model for all the locations considered. Four different ANN models were developed for each location in Keras python using all the input parameters. The evaluation metrics used in this study are; R, R-squared, RMSE, and MAE. The models developed are capable of predicting solar irradiance and solar PV parameters. The R values for the ANN models range from 0.9046–0.9777 for solar irradiance and 0.7768–0.8739 for solar PV multi-parameters prediction.

As an important part of the financial sector, banking systems play a critical role in economic development as well as in improving the quality of life of the people of sub-Saharan Africa (SSA). However, little evidence exists in the literature about the performance of the banking systems in SSA compared with developed economies. This paper investigates the effects of economic development on banking performance across 23 SSA countries and 14 developed countries between 1981 and 2018. Using estimation models such as feasible general least squares, fixed-effects estimation with Driscoll-Kraay standard errors, and system and difference generalized method of moments, our findings show that in SSA as a whole, economic development has a positive impact on commercial bank lending, raises lending rates, increases the amount of domestic credit to the private sector (DCPS), and reduces the number of nonperforming loans (NPLs). In developed countries, economic development has a positive impact on lending rates, DCPS, and the number of NPLs. However, although economic development in developed economies has mostly positive effects on banking performance, SSA countries have more performance issues in banking as a whole and in regional pockets. A careful reassessment of SSA’s interregional banking system is therefore highly recommended. An increase in the number of NPLs in East and Southern Africa, a decline in deposit rates offered in Southern, West, and Central Africa, and increasing real interest rates in SSA despite economic growth are some of the concerns that require careful reassessment and policy adjustments.

Water abandonment in hydroelectricity production is a major challenge that can be solved with an increase in electricity demand. China as a country with huge hydropower installation is faced with the problem of underutilizing the hydropower potential due to inadequate electricity demand and transmission facility. In this study, we investigate the potential of hybridizing hydrogen production with hydropower stations in Southwestern China. We found that the integration of hydrogen production with hydropower stations will help reduce the country's CO2 emissions and as much as 1.18% reduction in China emission can be achieved adopting this methodology. In a hydropower station of 750 MW capacity, about 3.142 × 108 kg of hydrogen could have been produced from the abandoned water in 2019. This will also result in 351, 734, 330.9 kgCO2/yr emission reduction. We also developed a model to determine the optimized hydrogen installed capacity based on different parameters. Based on 2019 data, the CO2 emission of China will be reduced by 0.127% with the production of hydrogen from the excess electricity of a 750 MW hydropower station (Case study A) in Sichuan Province.

Unlike previous studies where models/methods used in determining the carbon emission are presented, in this paper, a detailed analysis of the causes, trends, and solutions to carbon emission in Africa is presented. Economic development plays a crucial role in the well-being of a country/continent, thereby, affecting energy consumption. The impact of economic development on Africa's carbon (CO2) emissions trend is first investigated. After which, three neural network models are developed to predict the future trend of total CO2 emission in the continent. Then, the use of renewable energy (RE) sources for power generation is analyzed/proposed as a viable solution for CO2 emission reduction in Africa. Finally, the impact of battery electric vehicles (BEVs) integration and hydrogen production in maximizing RE production in Africa's largest economy is analyzed. Secondary data of the economic indicators for twenty-five different African countries have been used to justify the effect of economic development on their carbon emission. From the results of the analyses, gross national income and carbon emissions in all sectors were found to be significantly positively correlated. That is, as national wealth across Africa increases, carbon emissions in the continent increase. Also, the predicted total annual CO2 emission showed that most countries will witness an increase in total CO2 emission by 2022 in comparison to 2018. The proposed RE-based method for power generation showed that the CO2 emission from the power industry can be reduced to zero for an African country. Nevertheless, the use of BEVs and the production of hydrogen will be integral in achieving this.

Through public-private partnership (PPP), social responsibility (SR) is crucial for developing sustainable public infrastructure for municipal solid waste management (MSWM). This study develops an SR framework for PPP success in MSWM. The study designs a picture fuzzy projection-based grey relational analysis method to rank the SR initiatives for PPP MSWM success. The study identifies nineteen SR initiatives from the literature and groups them into three dimensions: Environmental-based SR, Community-based SR, and Employee-based SR. Biodiversity and water protection, with a relative score of 0.8863, is the most important under the Environmental-based SR. Sanitation equipment provision has a relative score of 0.9095, and it is ranked first under the Community-based SR. Concerning the Employee-based SR, workers’ health and safety is the most important initiative, with a relative score of 0.8931. The findings inform scholars, companies, investors, and policymakers of the initiatives that need attention to promote sustainable development in solid waste management.

Over the last 20 years, development in China hasfollowed a positive trend. However, the high energy consumption has impacted the environment negatively. About 239 cities fell short of the national air quality standard in 2017 and the groundwater fell from quality 31.9% to 10.9% in 2018. In this study, the use of solar PV and onshore wind turbines for electricity generation in a particular area in China is analyzed. The integration of hydrogen production and Electric Vehicles (EVs) into the energy mix is dynamically modeled.Three storage mechanisms namely; vehicle-to-grid (V2G), V2G + batteries, and V2G + pumped hydro storage wasconsidered. EnergyPLAN computer program is used to perform a dynamic simulation in this research to analyze the potential carbon emission reduction of the proposed methodology. The optimal PV and on-shore wind turbine capacity required for a 1 TWh/yr electricity demand and 0.02 TW/yr EVs charge demand is 400 MW and 350 MW respectively. The storage capacities are 30 GWh of V2G, 55 GWh of V2G + Batteries, and 60 GWh of V2G + pumped hydro storage with various charge/discharge hourly profiles. The proposed methodology in this research will reduce the yearly coal consumption by 495.3 kton and yearly carbon emission by 368.2 kton.

The severe effects of climate change and its anticipated negative influence on the future of the globe has prompted more research into the attainment of carbon neutrality. While carbon neutrality is a paramount issue, human socio-economic well-being which is mostly influenced by economic activities cannot be overlooked. This study investigates the effect of financial sector activities on CO2 emission in five economic sectors and three economic bodies. The financial sector variables utilized are derived from the undertakings of institutions such as banks, stock exchanges, and insurance companies. Using a sample of 39 countries between 1989 and 2018, this paper provides a global perspective of the profound impact financial sector activities have in different economies on CO2 emission reduction. The feasible generalized least squares (FGLS) regression model, as well as the random and fixed effects model with regards to Durbin–Wu–Hausman, are used to analyze the data. The generalized method of moments (GMM) is also adopted as the robustness method. Our findings show that for emerging economies, all major activities of the financial sector aggravated CO2 emission levels in all major CO2 emitting economic sectors. The developing and developed economies also show a similar trend. In the emerging economies, virtually all activities carried out by the financial sector have a significant negative impact on CO2 emissions at the 1% or 5% significance level, thereby hampering CO2 emission mitigation efforts. However, increased long-term bank lending to non-major economic sectors is found to alleviate CO2 emissions in developing economies. This is also the situation with increased numbers of import insurance. Meanwhile, CO2 emissions are found to decrease with increased net portfolio investments and numbers of insurance on exports. These findings not only imply that financial sector activities play a fundamental role in CO2 emission mitigation but also serve as a reminder for financial policymakers that the decisions they make have an inevitable impact on the attainment of carbon neutrality in their economies.

Firm's performance is not only influenced by sound decisions but also by institutional processes. This study argues that the effect of government intervention on acquisition returns is partly dependent on institutional factors such as provincial marketization and the legal environment. This study employs ownership and merger and acquisition regulations issued by the Chinese government 2003–2018 to measure government intervention and also employs both the OLS and the random effects technique. The result shows a positive relationship between ownership intervention and acquisition returns. Next, the study finds that variances in regional marketization and the legal environment strengthen this relationship such that acquirers in high marketization regions and acquirers in a robust legal environment earn higher returns than acquirers in a low marketization region and acquirers in a weaker legal environment. The results imply that acquisition returns are not even across all locations in a transitional economy. Ongoing policies should aim at bridging the variances in different locations as it impacts acquirers' returns.

In recent years, the developed, emerging, and developing economies have prioritized environmental sustainability attainment. In an attempt to offer some potential policy choices towards the achievement of sustainable development, this paper shifts emphasis from the popularly discussed economic development and carbon emissions nexus. Instead, we examine the impact of the banking and financial system’s activities on carbon emissions for a sample of 45 countries. These are comprised of developed, emerging, and developing countries between 1990 and 2017. To fill the gap in the literature, the nexus is examined in seven different phases. This study exposes robust and reliable empirical results with the use of Feasible General Least Squares, random effects with regards to the Durbin–Wu–Hausman test, and Difference General Method of Moments panel data estimation models. Our findings indicate that the increase of domestic credit to the private sector and commercial bank lending consistently contributes towards aggravated carbon emissions in all economic types. Additionally, increased deposit rates in developing economies, increased lending rates in developed economies, and increased deposit rates in emerging economies contribute towards the overall reduction of carbon emissions. The decrease in lending to high GHG emitting members of the private sector by financial institutions in all economies is recommended based on the results of this study.

Global energy demand has consistently increased in recent decades, owing to the rapid population increase. Energy consumption is higher than it has ever been, and most fossil supplies are on the verge of exhaustion with the current rate of exploitation. Facing the double pressure of meeting energy demands and reducing carbon emissions, the integration of renewable energy into different aspects of the energy ecosystem has become a unified agreement for all countries globally. Hybrid renewable energy systems that integrate multiple energy sources can effectively solve this problem. In the hybrid renewable energy system, optimizing the unit size is the key to achieving efficient utilization of renewable energy. Research trends show that artificial intelligence methods are gaining attention from researchers and can provide good system optimization in the absence of long-term weather data to provide good system optimization. While different studies have presented articles in this research domain, it is important to give a comprehensive collation/summary of the research trend while highlighting key models/methods utilized in this research domain. Hence, based on the published literature, this paper provides a comprehensive bibliographical review of the current trends/status of hybrid renewable energy systems research. Key research articles published in the Web of Science between the years 2000 and 2022 in this research domain have been reviewed. This paper describes the hybrid renewable energy systems, summarizes many different energy systems in existing literature, compares the differences between various energy systems, and analyzes the physical models of different systems, as well as the optimization methods and the optimization of the systems. Further, the uncertainty of electricity generation from renewable energy sources is analyzed in the literature review and the future challenges of hybrid renewable energy systems are summarized.

As the world faces pressing climate and energy challenges, Artificial Intelligence is proven as a transformative force in advancing renewable energy systems. This study reviews the current and future applications of Artificial Intelligence in renewable energy, highlighting its transformative role in enhancing the efficiency, reliability, and scalability of renewable energy systems. The study draws from over 400 recent publications, selected based on their relevance to Artificial Intelligence and renewable energy systems. We discuss the use of Artificial Intelligence techniques including machine learning, deep learning, and reinforcement learning models for optimizing energy production, forecasting demand, predictive maintenance, and managing decentralized energy systems. Emerging fields such as quantum machine learning and Artificial Intelligence-augmented reality are also considered because of their potential to transform energy infrastructures. The survey reviews significant innovations in wind and solar energy, energy storage, and smart grid technologies, focusing on how Artificial Intelligence addresses challenges like intermittency and variability. Furthermore, we discuss the importance of big data, the Internet of Things, and real-time analytics in advancing Artificial Intelligence models, along with the evolving landscape of Artificial Intelligence-driven policy and market modeling for renewable energy adoption. Real-world case studies, like Google’s collaboration with DeepMind for optimizing wind energy output and Australia’s National Electricity Market integrating Artificial Intelligence for grid stability, underscore the practical impact of Artificial Intelligence in renewable energy. This paper highlights challenges that are hindering Artificial Intelligence adoption in renewable energy systems and offers recommendations for improving the available technology to maximize Artificial Intelligence’s potential in promoting sustainable energy and addressing climate change.

Persistent gender disparities in science, technology, engineering, and mathematics (STEM) remain a global concern, but their occurrences vary markedly across socio-cultural and geographic contexts. This paper presents a systematic review of literature on gendered participation in STEM higher education in the United Kingdom and Ghana, with a focus on barriers, facilitators, and opportunities for reform. Using structured searches across peer-reviewed and grey literature (2015-2025), 33 studies were identified and synthesised through reflexive thematic analysis. Findings reveal persistent challenges: early subject segregation, entrenched stereotypes, micro-level bias, opaque leadership pathways, and uneven access to digital skills, employability support, and mobility opportunities. Geography shapes how these barriers are experienced-UK research emphasises implicit bias, prestige economies, and work-life constraints, while Ghanaian evidence highlights patriarchal norms, rural-urban inequalities, and resource limitations. Despite contextual variation, a shared set of facilitators emerges, including gender-responsive curricula, mentorship, gender-mainstreamed quality assurance, equitable AI/digital skilling, and targeted mobility schemes. The review also identifies critical research gaps, underscoring the importance of context-sensitive yet transferable strategies to advance gender-transformative policy, pedagogy, and institutional reform. We argue for a transversal learning approach-adapting effective practices bi-directionally-to inform gender-transformative policy and programme design that is locally responsive with transnational ties.