As digital twins emerge to provide a replication of physical assets in the digital space, the application of predictive maintenance of industrial asset becomes more effective. Developing digital twins for the predictive maintenance case study leverages Internet of Things, cloud computing and machine learning. While these technologies extend the necessary tools for deploying predictive maintenance digital twins, an enabling architecture facilitated by fog computing positions predictive maintenance digital twins for improved computational cost and latency than centralizing in the cloud or running locally at the edge. This work presents the application of a distributed digital framework, showing the benefits of better compute utilization and latency by adopting a distributed digital twin framework for predictive maintenance of wind turbine components in a wind farm.

Investors’ attitudes were severely altered by the Covid-19 crisis, which had an intolerable effect on volatile returns in Nigerian stock market and other stock markets worldwide. Psychological trauma was experienced by stock investors due to the inability to foresee returns following the worldwide shock of Covid-19 and the subsequent drop in stock investments brought on by the unpredictability of macroeconomic behavior. This study analyzed the link effect of investor psyche on stock returns in Nigeria between March 2019 and December 2022. For this research, Sentiment Index (SMI) model was employed for the connection between investors psyche and returns on the Nigerian stock market. It was found that, investors psyche during Covid-19 pandemic negatively affected stock returns, resulting from negative concerns about their survival and financial security than the stock market, thus triggered sharp decline in stock investment. Thus, study concluded that Covid-19 outbreak impacted stock returns negatively. The research recommends that the Nigerian stock exchange commission should increase disclosure of market information, which is related to the magnitude and potentials of stock returns and degree of volatility at all times. The concerned volatility and potentials during the Covid-19 era were also influential to enhance investors’ confidence.

This study uses a wind turbine case study as a subdomain of Industrial Internet of Things (IIoT) to showcase an architecture for implementing a distributed digital twin in which all important aspects of a predictive maintenance solution in a DT use a fog computing paradigm, and the typical predictive maintenance DT is improved to offer better asset utilization and management through real-time condition monitoring, predictive analytics, and health management of selected components of wind turbines in a wind farm. Digital twin (DT) is a technology that sits at the intersection of Internet of Things, Cloud Computing, and Software Engineering to provide a suitable tool for replicating physical objects in the digital space. This can facilitate the implementation of asset management in manufacturing systems through predictive maintenance solutions leveraged by machine learning (ML). With DTs, a solution architecture can easily use data and software to implement asset management solutions such as condition monitoring and predictive maintenance using acquired sensor data from physical objects and computing capabilities in the digital space. While DT offers a good solution, it is an emerging technology that could be improved with better standards, architectural framework, and implementation methodologies. Researchers in both academia and industry have showcased DT implementations with different levels of success. However, DTs remain limited in standards and architectures that offer efficient predictive maintenance solutions with real-time sensor data and intelligent DT capabilities. An appropriate feedback mechanism is also needed to improve asset management operations.

This study uses a wind turbine case study to showcase an architecture for implementing a distributed digital twin in which all important aspects of a predictive maintenance solution in a DT use a fog computing paradigm, and the typical predictive maintenance DT is improved to offer better asset utilization and management through real time condition monitoring, predictive analytics, and health management of selected components of Wind turbines in a wind farm.. Digital twin (DT) is a technology that sits at the intersection of Internet of Things, Cloud Computing and Software Engineering to provide a suitable tool for replicating physical objects in the digital space. This can facilitate the implementation of asset management in manufacturing systems through predictive maintenance solutions leveraged by Machine Learning (ML). With DTs, a solution architecture can easily use data and software to implement asset management solutions such as Condition Monitoring and Predictive Maintenance using acquired sensor data from physical objects and computing capabilities in the digital space. While DT offers a good solution, it is an emerging technology that could be improved with better standards, architectural framework, and implementation methodologies. Researchers in both academia and industry have showcased DT implementations with different levels of success. However, DTs remain limited in standards and architectures that offer efficient predictive maintenance solutions with real time sensor data, and intelligent DT capabilities. An appropriate feedback mechanism is also needed to improve asset management operations.

Abstract Background Cholera, a diarrheal disease caused by the bacterium Vibrio cholerae, transmitted through fecal contamination of water or food remains an ever-present risk in many countries, especially where water supply, sanitation, food safety, and hygiene are inadequate. A cholera outbreak was reported in Bauchi State, North-eastern Nigeria. We investigated the outbreak to determine the extent and assess risk factors associated with the outbreak. Methods We conducted a descriptive analysis of suspected cholera cases to determine the fatality rate (CFR), attack rate (AR), and trends/patterns of the outbreak. We also conducted a 1:2 unmatched case–control study to assess risk factors amongst 110 confirmed cases and 220 uninfected individuals (controls). We defined a suspected case as any person > 5 years with acute watery diarrhea with/without vomiting; a confirmed case as any suspected case in which there was laboratory isolation of Vibrio cholerae O1 or O139 from the stool while control was any uninfected individual with close contact (same household) with a confirmed case. Children under 5 were not included in the case definition however, samples from this age group were collected where such symptoms had occurred and line-listed separately. Data were collected with an interviewer-administered questionnaire and analyzed using Epi-info and Microsoft excel for frequencies, proportions, bivariate and multivariate analysis at a 95% confidence interval. Results A total of 9725 cases were line-listed with a CFR of 0.3% in the state. Dass LGA had the highest CFR (14.3%) while Bauchi LGA recorded the highest AR of 1,830 cases per 100,000 persons. Factors significantly associated with cholera infection were attending social gatherings (aOR = 2.04, 95% CI = 1.16–3.59) and drinking unsafe water (aOR = 1.74, 95% CI = 1.07–2.83). Conclusion Attending social gatherings and drinking unsafe water were risk factors for cholera infection. Public health actions included chlorination of wells and distribution of water guard (1% chlorine solution) bottles to households and public education on cholera prevention. We recommend the provision of safe drinking water by the government as well as improved sanitary and hygienic conditions for citizens of the state.

Cryptocurrencies have gained popularity and are increasingly used in the global financial system, despite their volatile nature. They have become an attractive financial instrument for individuals and corporations due to their potentials for high returns, decentralized nature, and exemption from strict government regulations. This study aims to investigate how cryptocurrency volatility affects the performance of companies listed on the Nigerian Exchange Limited (NGX). The study uses an ex post facto research design and the GARCH (1,1) model. Weekly data on Bitcoin and Ethereum were obtained from www.ng.investing.com and used to construct a cryptocurrency composite index with principal component analysis (PCA). The All-Share Index data were extracted from the Security and Exchange Commission (SEC) statistical bulletin between January 2017 and December 2021. The result of the mean equation shows that cryptocurrency trading in Nigeria responds more to positive sentiment and good news than bad news, while the variance equation reveals that current conditional volatility of cryptocurrencies and companies' performance is influenced by their previous shocks and past volatility conditions. The study also found evidence of volatility clustering in companies’ performance on the NGX. Therefore, investors are advised to exercise caution in an expanding cryptocurrency market, while regulators and policymakers should use relevant indicators to avoid contagion risk that could spread to the stock market. This paper is significant and relevant to achieving the Nigerian government's plan to introduce an official virtual currency.

The nexus between stock return and macroeconomic indicators have been a debatable phenomenon for a long time. It has also be one of the major issues for domestic and foreign investors in building efficient and optimum stock portfolio in the capital market. However, the unstable macroeconomic indicators adversely affect the level of stock return in the Nigerian capital market. Against this background, this study investigates the effect of macroeconomic factors on stock return in the Nigerian capital market. The study employed secondary data obtained from Nigeria Stock Exchange (NSE) fact book and Central Bank of Nigeria (CBN) statistical bulletin within the period of 1998 to 2019. The data obtained was subjected to Autoregressive Distribution Lag (ARDL) method of analysis. Findings revealed that money supply and aggregate industrial production positively and significantly affect stock return with co-efficient and P-value at (β=0.466098, P<0.05; β=0.213141, P<0.05) while exchange and inflation rates negatively affect stock return in the Nigerian stock exchange market with co-efficient and P-value at (β=-0.009285, P<0.05; β=-0.028260, P<0.05) respectively. The study concludes that macroeconomic factors significantly affect stock return in the Nigerian stock market at both the short run and long run. The study recommends that the Central Bank of Nigeria should employ deflationary fiscal policy and Adaptive Stabilization Method of Exchange Rate policy in order to reduce variance between actual and expected stock returns in the Nigerian Stock Market.

The practice of ritual killings has assumed an alarming rate in Ilorin City and continues to pose serious threats to human lives. Many studies have been conducted on ritual killings in many Nigerian states, with few studies in Ilorin, partly because of its historical attribute of being the land of Islamic clerics. Unfortunately, these clerics (Imams and Pastors) are becoming increasingly involved in the gruesome act. This paper examines the trends of ritual killings in Ilorin and the involvement of politicians and religious clerics. Holding to anomie-strain theory, 29 respondents (eight from Pakata, nine from Agaka, and 12 from Adewole) were conveniently selected for an in-depth interview (IDI). Content analysis reveals the buying and selling of human parts for rituals as a trending business in Ilorin that involves men and women, including youths. The increase in rituals is attributed to the severe use of human parts and bodies to prepare spiritual concoctions by religious clerics for some vague reasons. It also reveals Politicians are also among the major perpetrators. Therefore, the research, among others, recommends a ‘policy-based action’ that would involve amending the constitution to prescribe only death penalty for ritual killings. For public deterrence, anyone from any family with a traced case of ritual killing should not be allowed to hold public office.

Wind turbines are one of the sustainable means of renewable energy which have a life span of many years. Despite their durability, there is a need for maintenance to prevent downtime. With industry 4.0, wind turbines are usually implemented with smart technologies that connect them to base stations enabled with information systems for management. The implementation architecture usually adopted for this is centralized and does not factor in the need for intelligence at the edge of the network where minute detail of defects can be observed in real-time. This centralized approach also increases the cost of computation and latency. Overall, this indicates a negative effect on the efficiency of wind turbine asset management. This paper proposes the use of Fog computing architecture to implement a system of monitoring the unit and system level operations of the wind turbine plant using digital twins. The proposed concept attempts to improve the performance of the wind turbines by using decentralized intelligence at the fog nodes, where sensor data is pre-processed before sending it to the global digital twin in the cloud for deeper insights.

We propose a Smart IoT framework for gunshot detection. The Smart gunshot system detects gunshot sounds and transmits sound signals to a data centre where a trained machine learning model classifies the sound signal data and sends alerts to the base station where the response monitoring system deployed will be notified for further action. This study focuses on the design of the conceptual Smart gunshot detection framework. When implemented, the study will help in mitigating crimes and insurgency in societies. Our use case is against armed banditry in Northwestern Nigeria.

This paper proposes a multi-drone surveillance coordination algorithm that controls the random behaviour of naturally-inspired strategies. The main drawback of naturally inspired strategies, such as Levy flight, is the agents' poor coordination and redundant search (continuous exploration of single location within minimum time and space). The redundant search was removed by assigning probability values to cells and subjecting agents' waypoints acceptance to distance and time threshold. The proposed algorithm ensures broad coverage, minimised redundant search, scalability, and adaptable swarm coordination. The benefits of the algorithm were evaluated on the simulation of the Nigerian telecommunication masts surveillance mission using a proposed architecture. Future work focuses attention on the implementation of algorithms on real drones and sensor information organisations.

The nexus between agency banking strategies and financial inclusion have been a debatable paradox for a long period of time due to the important role play by deposit money banks in finance inclusive economy functions. However, the goal of financial inclusion has not been achieved due to geographical distance of banks to rural area, poor bank innovation and technological advancement to rural settlement. The study investigates the effect of agency banking strategies (bank innovation strategy, geographical coverage strategy, and technological advancement strategy) on financial inclusion in rural areas in Kwara State, Nigeria. The study employed primary data obtained from respondents through administration of questionnaire within the period of 2019 and 2020. The data obtained were subjected to reliability and validity tests as well as Tobit Regression method of analysis. Findings revealed that agency banking strategies such as bank innovation strategy, geographical coverage strategy and technological advancement strategy have positive and significant effect on financial inclusion of rural areas in Kwara State, Nigeria. The study concludes that agency banking strategies enhance financial inclusion of rural areas in Kwara State, Nigeria. The study recommends that deposit money banks management should extend bank innovative products or services and enlighten the rural segment entrepreneurs on bank inclusion strategies so as to increase inclusive financial services and economic activities for the rural segments.

Stable macroeconomic environment is critical to an efficient stock market and economic growth. Banking sector plays important role in sustaining the growth in Nigerian stock market. However, the effect of unstable macroeconomic factors on the sector's stock returns over the years cannot be ignored. This study seeks to investigate banking sector's stock price behavior in response to unstable macroeconomic variables in Nigerian stock market. Autoregressive Distributed Lag (ARDL) model was employed to examine both short run and long run effects on the study variables between 2009 and 2018. Findings revealed negative significant effects of interest rate and foreign reserves on the stock price behavior of the banking sector both in the short run and long run with -0.21 and -9.004 respectively. Inflation rate has positive significant influence of 0.42, while exchange rate is not statistically significant in influencing stock price behavior in Nigerian stock market, all at 1% level of significance. The study concludes that, banking sector stock price is being influenced by foreign external reserve, interest rate and inflation rate. The study recommends that monetary policy rate should be reduced in order to lower the cost of borrowings and enhance liquidity level in the stock market. Keywords: ARDL Co-integration, Banking sector, Macroeconomic indicators, Nigerian stock market, Stock price behavior JEL Classifications: E02, G12 DOI: https://doi.org/10.32479/ijefi.9041

Coronavirus Disease 2019 (COVID-19) Pandemic is ongoing, and to know how far the virus has spread in Niger State, Nigeria, a pilot study was carried out to determine the COVID-19 seroprevalence, patterns, dynamics, and risk factors in the state. A cross sectional study design and clustered-stratified-Random sampling strategy were used. COVID-19 IgG and IgM Rapid Test Kits (Colloidal gold immunochromatography lateral flow system) were used to determine the presence or absence of antibodies to SARS-CoV-2 in the blood of sampled participants across Niger State as from 26th June 2020 to 30th June 2020. The test kits were validated using the blood samples of some of the NCDC confirmed positive and negative COVID-19 cases in the State. COVID-19 IgG and IgM Test results were entered into the EPIINFO questionnaire administered simultaneously with each test. EPIINFO was then used for both the descriptive and inferential statistical analyses of the data generated. The seroprevalence of COVID-19 in Niger State was found to be 25.41% and 2.16% for the positive IgG and IgM respectively. Seroprevalence among age groups, gender and by occupation varied widely. A seroprevalence of 37.21% was recorded among health care workers in Niger State. Among age groups, COVID-19 seroprevalence was found to be in order of 30-41 years (33.33%) > 42-53 years (32.42%) > 54-65 years (30%) > 66 years and above (25%) > 6-17 years (19.20%) > 18-29 years (17.65%) > 5 years and below (6.66%). A seroprevalence of 27.18% was recorded for males and 23.17% for females in the state. COVID-19 asymptomatic rate in the state was found to be 46.81%. The risk analyses showed that the chances of infection are almost the same for both urban and rural dwellers in the state. However, health care workers and those that have had contact with person (s) that travelled out of Nigeria in the last six (6) months are twice ( 2 times) at risk of being infected with the virus. More than half (54.59%) of the participants in this study did not practice social distancing at any time since the pandemic started. Discussions about knowledge, practice and attitude of the participants are included. The observed Niger State COVID-19 seroprevalence means that the herd immunity for COVID-19 is yet to be achieved and the population is still susceptible for more infection and transmission of the virus. If the prevalence stays as reported here, the population will definitely need COVID-19 vaccines when they become available. Niger State should fully enforce the use of face/nose masks and observation of social/physical distancing in gatherings including religious gatherings in order to stop or slow the spread of the virus.

The investment decision in the Nigerian stock is based on the level of volatility of the market. However, the volatility persistence of stock returns in the Nigerian market has negatively affects the participation of investors in the market. This study re-examined the effect of persistent volatility on the prices of the stocks in the Nigerian market between 2008 and 2018. With the use of ARCH and GARCH estimations, the study revealed three distributional assumptions with the co-efficients as (0.897, 0.939 and 0.956) revealing that the returns exhibit high volatility persistence at different selection criterion models. It concludes therefore, that the Nigerian stock market exhibits high volatility persistence. Hence, the study recommends that the regulators in the Nigerian stock market should model the regulatory framework guiding the operations in line with emerging markets with less volatile stock returns.

The nexus between development of stock market and the performance of economic activities have been a critical issue around the globe.The issue as to whether a well-developed stock market influences the performance of economic activities and the relevant of developmental policy strategies have been a concern for the developing economies especially the West Africa economies.Literatures have shown that most of the West Africa economies were faced with sharp swings and wide fluctuations in the stock market indices, which had negative effect on the performance of economic activities of these economies.This study examined the effect of stock market development on the economic performance among West Africa economies.The study employed secondary data subjected to panel regression method of analysis within the period of seven years across selected West-Africa countries.Findings revealed that stock market development indicators; ratio of market capitalization to gross domestic product, All Share Index, and Stock Turnover have positive effect on economic performance while corruption perception index have negative effect on economic performance, all at 5% level of significance.The study concludes that stock market development indicators and corruption index affect economic performance in West Africa.Therefore, the study recommends that the stock market regulatory authorities should initiate strategic policies that would boost market liquidity and encourage easy access of companies to the market and also be more

This research examined the effect of market liquidity, inflation, and exchange rates on stock return in Nigerian Stock Exchange market. The researchers used ex-post facto design and employed secondary data subjected to Auto-regressive Distributive Lag (ARDL) bound test method of analysis within the period of twenty-one years. Findings reveal that in the short run, stock turnover, trading volume, exchange, and inflation rates have affected stock return positively and significantly. In the long run, market turnover has a positive effect. However, inflation and exchange rates have affected stock return negatively and significantly. Then, trading volume has a negative but insignificant effect on stock return, which is all at 5% level of significance. The researchers conclude that market liquidity, exchange, and inflation rates affect stock return. Therefore, the researchers recommend demutualization and transparent structures and adaptive method stabilization in exchange rate policies to increase stock market patronage, minimize transaction costs, and mitigate the market uncertainties.

A survey was conducted on nematodes associated with soils of farmlands around Kware Lake, Kware local government area of Sokoto State. The aim this research is to determine the species composition and distribution of nematodes in farmlands around Kware Lake. Sampling was carried out from July to September, 2016. Soil samples were collected to a depth of 0-15 m. Centrifugation technique was used for the extraction of nematodes and viewed under the microscope. Results indicates that seven species were detected whose richness varied with respect to sites. The nematodes detected were Xiphinema spp., Heterodera spp., Trichodorus spp., Meloidogyne spp., Rotylenchus spp., Longidorus spp. and Pratylenchus spp. The most abundant nematodes isolated were Meloidogyne spp. (29.63%) while Pratylenchus spp. were found to be least abundant (2.22%). This is because Meloidogyne was very widely distributed and affect a wide variety of crops while Short Research Article Magami et al.; AJRIB, 2(4): 1-8, 2019; Article no.AJRIB.53337 2 Pratylenchus was a migratory endoparasite and can be ascertain by its high population in the root. There was no significant difference on nematode species occurrence between the three sampling sites at p<0.05. The result showed there was wide diversity of nematodes inhabiting the study area with diversity index of 1.77. The presence of plant parasitic nematodes in the soils of farmlands around Kware Lake highlights need for prevention and control of nematode species, so as to reduce the risk of losses to agricultural yield.