Recent publications
This chapter focuses on the communication strategies used by development agencies and partners in the development and implementation of sustainable development programmes, evaluating the impact these strategies have on health development and poverty alleviation in communities in Rivers State. Referencing the development media theory, the democratic participant theory and the communication for social change theory, the chapter examines four development agencies and partners; the Niger Delta Development Commission, The World Bank, The World Health Organization and Indorama Eleme Petrochemical Limited. The qualitative study design used (un-structured) in-depth interviews with staff from the organisations studied, and focus group discussion sessions were held with community dwellers in four local government areas. Findings showed that development agencies and partners used a variety of communication strategies, tools, and methods to communicate development initiatives.
This paper investigated Librarians’ Awareness towards the Use of Artificial Intelligence Technologies for Sustainable Library Services. Four research objectives and one null hypothesis were formulated to guide the study. The study adopted a descriptive survey research design. The targeted population of the study are Librarians in Nigeria. Questionnaires were sent online via Google form to the association WhatsApp platforms in order to get responses from the members. The process brought in a total of 203 responses which was used to analyse the data. Mean and standard deviation were used to answer the research questions while t test was used to test the hypotheses at 0.05 level of significant. The finding shows that there is high extent of the level of librarians’ awareness in the use of AI technologies in library services. The study also highlighted the various challenges of using AI in library operations, ranging from the fact that AI do not have human feelings/physical contact and frequent use of AI can make them irrelevant in the library thereby losing their job. Finally, the hypothesis stated that there is a significant difference between the mean score of the awareness of librarians towards the use of artificial intelligence technologies on library services. Since the p-value is less than the significance level, the null hypothesis is rejected. Based on the findings, the researchers recommends among others that there is need for librarians to attend trainings, workshops and conferences related to the adoption of artificial intelligence in order to prepare them for future tasks.
Alzheimer's disease (AD) is a progressive neurological condition characterized by a loss in cognitive functions, with no disease-modifying medication now available. It is crucial for early detection and treatment of Alzheimer's disease before clinical manifestation. The stage between cognitively healthy older persons and AD is known as mild cognitive impairment (MCI). To predict the transition from one-stage MCI to probable AD, five ensemble learning approach was used (Stacking, Gradient boost Bagging, Adaptive boost and Voting), an integrated model that combines not only cross-sectional neuroimaging biomarkers at baseline but also longitudinal cerebrospinal fluid (CSF) and cognitive performance biomarkers from the Alzheimer's Disease Neuroimaging Initiative cohort (ADNI). The adaptive boost, stacking and bagging ensemble approach has shown potential to identify those at risk of developing Alzheimer's disease, this would benefit them the most from a clinical trial or to use as a stratification approach inside clinical trials.
In this analysis, we analytically obtain the eigenvalue solutions of non-relativistic quantum particles interacting with Mie-type potential in a topological defect geometry. Afterward, we study the thermodynamic properties for the quantum system, such as the vibrational free energy, mean energy, entropy, and specific heat using the partition unction and analyze the effects of topological defect. Furthermore, we calculated the Shannon entropy for this quantum system, thus measuring the loss of information about the particle’s location. This measure of information reveals the influence of the topological parameter on the location of the particle and provides us with more detailed parameters for a possible experimental detection of these quantum particles.
In this paper, we explore the relativistic quantum dynamics of spin-0 oscillator fields within the framework of a rotating cosmic string space–time, taking into account the presence of scalar and vector potentials. We solve the Klein–Gordon oscillator equation in this curved space–time, incorporating generalized versions of the Yukawa and Hulthen potentials as vector and scalar potentials, respectively. The analytical solution of the quantum system yields the energy eigenvalue and corresponding eigenfunction, which is expressed in terms of confluent Heun functions. The resulting energy spectrum is shown to depend on various parameters associated with the cosmic string space–time and the confining potentials. We graphically illustrate how the energy spectrum varies with changes in both the potential parameters and the cosmic string parameters.
The idea that spurred the author's interest in carrying out this study results from understanding a cashless society, where users of banking systems only move around with their ATM cards. With their ATM cards, they can access their money anywhere globally. This approach could resonate in libraries, where we now have a paperless society, having cost-benefit analysis in the era of the fifth Industrial Revolution (5IR). The 5IR has to do with humans interacting between technology or machines either to solve individual and organizational societal problems from a general or specific perspective. The interaction between humans and technology would assist in recognizing the synergistic in addressing the support for task accomplishment. The methodology applied is in two stages, first, a systematic literature review and second, interpretive content analysis of literature harvested from the database of Scopus. Findings revealed that technological advancements have enabled paperless libraries such that on-screen reading is increasingly prevalent, rapid development support through mobile computing, software, and internet/connections/Wi-Fi, multifarious electronic information retrieval among others. Furthermore, other findings were anchored on the relevance of paperless libraries in the 5IR, the functionality of paperless library systems, enhanced accessibility, and inclusivity, collaboration and connectivity, and environmental impact. Meanwhile, certain challenges and mitigation strategies were proffered. The study concludes that as we navigate this transformative era, the emergence of paperless libraries stands as a testament to the harmonious integration of technological prowess and human aspirations. Therefore, envision human-machine interactions be encouraged to bring about paperless libraries, considering the significance of AI and human-centric innovation.
This study explores the potential prospects and challenges of integrating Artificial Intelligence (AI) into children’s radio programmes in Rivers State, Nigeria. It perceives children’s radio programmes as pivotal to the transformation of young minds which is needed to pilot nation building and national reforms. It specifically took excerpts from the educational and entertainment children’s radio programme known as Kiddies-Zone (K-Zone) broadcast on Treasure FM, 98.5. It is a radio-show designed for children-listeners between 4 and 12 years old. It examines the role of AI in enhancing content creation, accessibility, and engagement in the context of children’s radio programming. However, the crux of deploying AI in this specific domain presents unique hurdles, encompassing infrastructure limitations, content appropriateness, access disparities, and data privacy concerns. This research, informed by existing literature and interviews with industry experts, underscores the complexity of these challenges and offers recommendations to effectively address them. Addressing these challenges requires a collaborative effort involving government, private sector organizations, and civil society. Investments in infrastructure development, cultural sensitivity, and digital inclusion are essential to ensuring that AI benefits all children, regardless of their geographical locations or socioeconomic background. Additionally, robust data protection regulations and security measures must be in place to safeguard children’s privacy and ensure responsible AI implementation.
Malaria and Typhoid fever are prevalent diseases in tropical regions, and both are exacerbated by unclear protocols, drug resistance, and environmental factors. Prompt and accurate diagnosis is crucial to improve accessibility and reduce mortality rates. Traditional diagnosis methods cannot effectively capture the complexities of these diseases due to the presence of similar symptoms. Although machine learning (ML) models offer accurate predictions, they operate as "black boxes" with non-interpretable decision-making processes, making it challenging for healthcare providers to comprehend how the conclusions are reached. This study employs explainable AI (XAI) models such as Local Interpretable Model-agnostic Explanations (LIME), and Large Language Models (LLMs) like GPT to clarify diagnostic results for healthcare workers, building trust and transparency in medical diagnostics by describing which symptoms had the greatest impact on the model's decisions and providing clear, understandable explanations. The models were implemented on Google Colab and Visual Studio Code because of their rich libraries and extensions. Results showed that the Random Forest model outperformed the other tested models; in addition, important features were identified with the LIME plots while ChatGPT 3.5 had a comparative advantage over other LLMs. The study integrates RF, LIME, and GPT in building a mobile app to enhance the interpretability and transparency in malaria and typhoid diagnosis system. Despite its promising results, the system's performance is constrained by the quality of the dataset. Additionally, while LIME and GPT improve transparency, they may introduce complexities in real-time deployment due to computational demands and the need for internet service to maintain relevance and accuracy. The findings suggest that AI-driven diagnostic systems can significantly enhance healthcare delivery in environments with limited resources, and future works can explore the applicability of this framework to other medical conditions and datasets.
Diverse challenges plague the public health space in Africa. Although some of these issues have been explored, the disruptive effects of recurring coup d'etats on the fragile healthcare sector have received little attention. This review investigates the historical prevalence and public health implications of military coups in Africa. The study reviews the period after World War II and highlights the prevalence of coup attempts in Latin America, Asia, and Africa. Since 1950, Africa has witnessed 109 successful coups. Despite a decline in coup success rates following the Cold War, attempts continue, fueled by a complex interaction of socioeconomic and political factors. The effects stem from the aftermath of these events and how they interrupt healthcare systems causing economic downturns and political instability. This review highlights the negative consequences of coups for maternal and child health, medical supply chain disruptions, and public health policy changes. Coups have also been demonstrated to aggravate poverty, unemployment, and healthcare professionals' emigration rates, worsening health disparities. Recent coups in Gabon, Niger, Guinea, and Burkina Faso have had implications on the long-term viability of democratic institutions and they have consequently impacted public health programs. This necessitates investigations into coups' role in forestalling the development and implementation of long-term public health plans. The reported outcomes include socioeconomic issues, disruptions in healthcare infrastructure , and the negative consequences on healthcare human resources management. Despite noteworthy advances in public health across Africa, the review argues that military coups constitute a substantial threat to the sustainability of these gains. It urges additional research, particularly longitudinal studies, to better understand the long-term impact on economic development and establish effective ways to prevent future coup attempts.
The Internet of Things (IoT) has continued to evolve as a highly disruptive technology by enabling smart connectivity beyond the realm of traditional computing devices. With the advancement in connected technologies driven by 5G and IoT, edge computing is thriving to usher in an interesting computing schemes including analysis, control, and storage, closer to the edge of a network and thereby aiding the resolution of latency and scalability issues. In recent times, IoT is consolidating spectrum of devices thereby offering new possibilities and capabilities not previously envisioned. The aim of this chapter is to explore, analyze, and contribute to the evolving landscape of IoT-driven analytics and edge intelligence in the context of autonomous navigation systems (ANS). The chapter aims to be a valuable resource that informs current understanding, highlights challenges, and guides future research and application of these technologies. We adopted the Systematic Literature Review (SLR). Forty-eight publications were selected from relevant repositories based on suitable predefined criteria. Findings show that the integration of meaningful analytics into ANS with edge computing capacity to provide broad remote control and regulation via a real-time sensing environment is still evolving.
Lately, public policy as a major political instrument has failed terribly in ensuring desirable internal security sector administration in Nigeria. This paper investigates the Nigerian public policy and internal security governance challenges using poverty, unemployment, gross domestic product (GDP), and foreign direct investment (FDI) as the baseline. The incident of poverty in Nigeria in 2020 reveals that the six geopolitical zones in Nigeria experience a severe rate of poverty, owing largely to an unprecedented rate of internal security problems (Olurounbi, 2021). The paper explores secondary research methods, secondary sources of data, and secondary data analysis (SDA) techniques. The paper reveals that Nigeria’s crucial economic metrics, such as poverty, unemployment, GDP, and FDI, have been significantly aggravated by the country’s poor internal security situation. As a result, residents are now experiencing significant economic hardship, negatively impacting Nigeria’s current internal security governance situation. The paper concludes that Nigeria’s internal security sector governance, particularly in the last ten years of democratic administration, has failed reasonably to meet Nigerians’ expectations. Following the findings, the paper advocates, among others, for a genuine electoral process capable of bringing in skilled people to public policy decision-making and program execution in Nigeria.
In this research, the synthesis, characterization, antimicrobial, and anticancer properties of iron oxide-nanoparticles were carried out in the presence of a bioreductant (Annona muricata). The preliminary formation of the iron oxide nanoparticles was investigated with an Ultra violet-Visible spectroscopy (UV) with absorption observed at wavelength of 236 nm. The X-ray diffraction (XRD) studies revealed peaks characteristic of Fe2O3, FeO2 and FeOOH. The nanoparticles showed elemental composition of C (32.1%), O (9.13%), Cl (32.36%), K (1.21%), Ca (1.03%) and Fe (24.16%). The microscopic studies showed spherical morphology and particle size of 4.19 nm. The antimicrobial analysis carried out revealed that some of the microorganisms were sensitive to the nanoparticles and are good antimicrobial agents. The anticancer analysis using normal HEK 293 cells and Hela carcinoma showed cell death of 50% at concentration of 63. 05 µg/mL for the HEK 293 cells and concentration of 48.97 µg/mL for the Hela cells.
Cholera has become one of the major global health challenges, especially in sub-Saharan Africa, where there is poor hygiene and sanitation, and due to the emergence of a resistant strain of the causative agent of cholera, there is a need for new therapeutic agents. Thiadiazoles are organic compounds that have been reported to have various biological applications. This study comprehensively analysed the structural, electronic, and biological properties of N1,N10-bis(5-(2-oxo-2H-chromen-3yl)-1,3,4-thiadiazol-2-yl)-decane-diamide, a thiadiazole derivative (TDZD) as an agent against cholera via theoretical approaches. Computational analyses were conducted employing the B3LYP/6-311 + + 2d,2p level of theory, which provided substantial insights. Vibrational assignments via FT-IR spectroscopy confirmed the excellent agreement between the theoretical and reported experimental values, confirming the structural stability of the ligand. The electronic property analysis revealed slight variations in the electrophilicity index of the compound across solvents, with the highest (5.790 eV) in water and the lowest (5.753 eV) in the gas phase. Additionally, the high electronegativity values in all solvents, following the order of water (4.640 eV), DMSO (4.639 eV), ethanol (4.637 eV), and gas (4.584 eV), indicated ligand reactivity. Furthermore, molecular docking results indicated distinctive interactions between the ligand and the 1XTC and 6EHB cholera receptor proteins. A higher binding score was observed between the ligand and 1XTC, with a binding score of -7.6 kcal/mol, than between the ligand and 6EHB, with a binding score of -7.1 kcal/mol. Furthermore, the drug amoxicillin (AMOX) showed a comparable binding score of -7.8 kcal/mol for 1XTC and − 7.4 kcal/mol for 6EHB. The obtained results suggest the biological potential of TDZD as an anti-cholera agent and can be the foundation for further studies.
In this study, we propose the exponential Kratzer–Feus potential and study the effect of the screening parameter on the diatomic molecules of CH, H2, NO, HCL, and LiH. We first solve the Schrödinger equation using the Nikiforov–Uvarov functional analysis method to obtain the energy eigenvalue. Interestingly, the proposed exponential Kratzer–Feus potential exhibits a repulsive interaction for diatomic molecules. We also compute the energy spectra for diatomic molecules for different values of the screening parameter α=0, 0.2, 0.4, 0.8, and 1.0. For a more complete study, we analyse the thermodynamic properties of the model. Furthermore, the quantum information measurements are calculated and used to study particle locations.
The concept of the Internet of Things (IoT) revolves around the exchange of information among low-power embedded devices, linked to the internet, in order to enable seamless communication. The IoT has a profound impact on various aspects of modern life, from mobile devices and sensors that keep track of the surrounding environment to smart industrial gadgets. While the Internet of Things offers numerous advantages, it also presents security and privacy concerns. The information transmitted through the IoT includes sensitive data such as banking information, geographic data, environmental data, medical information, and other personal information. Hence, it is crucial to acknowledge the security challenges posed by the IoT and address them appropriately. This chapter presents comprehensive insights into the security challenges associated with the Internet of Things, while considering the vast scope of the topic and existing literature. It discusses various IoT security challenges, IoT security architectures, IoT security solution trust zones and boundaries, potential risks of IoT devices, notable cases of IoT security breaches, solutions to IoT security breaches, strategies for securing IoT data and best IoT security practices.
The term “Industrial Internet of Things” (IIoT) is used to describe the network of sensors, industrial devices, and systems with the internet to enhance automation, efficiency, and optimization of industrial processes. IIoT has brought about significant improvements in industrial automation, operational efficiency, and cost reduction. However, the IIoT is vulnerable to cyber-attacks, which can cause significant disruptions to critical infrastructure, leading to financial losses and loss of life. This has made security a critical concern in IIoT systems. Hence, this chapter discusses Industrial IoT Security Threats and Counter Measures. In the chapter, essential ways to mitigate cyber-attacks on IIoT systems is presented and a comprehensive review and security strategy that includes risk assessment, threat modeling, and vulnerability testing as well as the proposed counter measure model. In the proposed model, the strategy incorporates security protocols and standards. Such as encryption, access control, and authentication, in a bid to guarantee the integrity, confidentiality, and availability of the IIoT system. Additionally, network segmentation is also introduced, which involves dividing the IIoT network into smaller, isolated segments that can be secured independently mitigating cyber-attack. The proposed model limits the impact of a potential breach and allows for more granular control over access to different parts of the IIoT system. In addition, the proposed model implemented an intrusion detection and prevention systems (IDPS) which helps in detecting and mitigating cyber-attacks by monitoring traffic, identifying potential threats, and blocking malicious activity. The proposed model is also useful in creating awareness about the various cyber-attacks; as well as presents essential countermeasures against cyber-attacks. Finally, the proposed scheme mitigates the risk of cyber-attacks on IIoT systems while ensuring the safety and reliable operations of critical infrastructure respectively.
Environmental issues have gained significant prominence on national, sub-regional, regional, and global agendas due to the recognition of the crucial role and advantages of environmental preservation in achieving sustainable development. The utilisation of environmental assessment is imperative for informed decision-making in developmental projects. It functions as a methodical process for recognising, predicting, and evaluating the ecological consequences of planned activities and initiatives. This process is executed prior to making substantial decisions and commitments. Biomonitoring presents a compelling method for evaluating environmental contamination inside the ecosystem. We will cover various biomonitoring methodologies, such as bioaccumulation, biochemical changes, morphological and behavioural observation, population- and community-level approaches, and modelling. The potential uses of biomonitoring mostly involve evaluating existing environmental contamination, aiding in bioremediation efforts, predicting toxicological effects, and investigating toxicological mechanisms through research. Therefore, this chapter will expound upon the subject of environmental assessment and provide an overview of biomonitoring. It will delve into the various techniques and approaches employed in environmental biomonitoring, as well as examine the factors that exert influence on this practise. Furthermore, it will explore the prevailing trends in biomonitoring, elucidate the concepts of bioindicators and biomarkers, and address the challenges and potential prospects that lie ahead for biomonitoring.
The possibility of remotely monitoring and managing chronic diseases is made feasible by the Internet of Things (IoT), which enables the collection and analysis of vast amounts of data from various sources such as sensors, devices, and wearables. However, conventional machine learning approaches often encounter difficulties when dealing with IoT data due to its noise, heterogeneity, and high dimensions. In this study, we introduce a novel technique for developing human‐centric intelligent systems for remote monitoring of chronic diseases using deep learning in an IoT context. This technique is capable of handling complex and diverse IoT data, providing accurate and comprehensible predictions and recommendations. We outline a standardized architecture for these systems, comprising of three distinct phases: data acquisition, data processing, and data visualization. To demonstrate the effectiveness of our methodology, we apply it to a case study involving the development and evaluation of a human‐centered intelligent system for remote monitoring of hypertension and diabetic patients, involving real users. Our findings reveal a substantial enhancement in prediction accuracy when employing our proposed hybrid algorithm. When compared to traditional algorithms, our proposed hybrid algorithm exhibits superior performance, achieving an accuracy of 93.5%, surpassing the Restricted Boltzmann Machine (RBM) with an accuracy of 88.2%, Long Short‐Term Memory (LSTM) networks at 90.4%, and Convolutional Neural Networks (CNN) at 91.6%. This improvement in accuracy is critical for ensuring reliable disease monitoring and management, highlighting the effectiveness of our approach. In terms of precision, scalability, and user satisfaction, we demonstrate the effectiveness and efficiency of our method. Furthermore, we address the social and ethical implications inherent in our methodology and suggest avenues for future research in this domain.
This study examined the relationship between resource management and economic development in Nigeria. Three research questions and three hypotheses were formulated to guide the study. The study adopted the correlational design. A sample size of 384 was derived from the population of the study which was determined using Krejcie and Morgan's 1970 table. Pearson Product Moment Correlation Coefficient was used to answer and also test the three null hypotheses at the 0.05 level of significance. The study reveals that there is a statistically significant relationship between resource management and economic development, as measured by per capita income, industrial progress, and the growth rate of national income. The study recommended among others that the countries should focus on developing strong institutions that can effectively manage natural resources as well as diversify their economies so that they are not overly reliant on natural resources.
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