Volume 14 Issue 2

This paper examines the potential of channeling Nigeria’s pension assets into marginal oil fields to drive economic growth and enhance pension contributors' financial security. Despite holding significant underutilized resources, Nigeria’s marginal oil fields face financial and operational constraints. Meanwhile, pension fund assets have grown exponentially, exceeding ₦20.48 trillion as of September 2024, largely invested (62.03%) in Federal Government Securities. However, federal government bond yields average 18.96%, compared to the oil and gas sector's 28.26% net profit margin, making marginal oil fields an attractive but high-risk investment option for pension funds. The Pension Reform Act of 2014 permits diversified investments, including equities and corporate bonds, yet energy sector investments remain limited. This study develops a conceptual framework assessing the viability of integrating pension assets into marginal fields, emphasizing risk management, return optimization and regulatory alignment. It examines the roles of key stakeholders, including government agencies, pension fund administrators and oil companies, in facilitating this model. Potential risks such as market volatility, regulatory changes and environmental concerns are addressed with strategies like diversification and adherence to ESG (Environmental, Social and Governance) principles. While benefits include higher returns and increased oil production, challenges in regulatory compliance, public perception and sustainability must be managed to ensure success. This analysis contributes to the discourse on alternative investment avenues for Nigeria’s pension assets, thereby offering policy recommendations for a mutually beneficial approach to pension fund involvement in marginal oil field development.

Purpose –Company performance can be measured in terms of financial and non-financial. Shareholders are motivated to invest their capital in the hope of getting a return in accordance with the capital invested. Indonesia is currently in a dilemma with a knowledge-based, fast-changing and technology-based economy. Most companies use technology to improve the efficiency of company activities and reduce costs incurred. Issues related to the environment and the adoption of green technology have received much attention over the past few years. The purpose of this study is to determine the relationship between Green technology, environmental disclosure and green intellectual capital on stock returns Design/methodology/approach –The type of data in this study is secondary data. The samples used in the study were Basic Materials and Transportation & Logistic companies with a sample size of 267. Findings –The results show no effect of green technology on stock returns. There is a significant negative relationship between Green Intellectual Capital and stock returns. While environmental disclosure has a significant positive effect on stock returns. Research limitations/implications –In developing countries, no statistically relevant relationship was found between fair valuation and earnings quality, which may be due to the adoption of IFRS and the lack of experience in fair valuation, or more generally, the very low influence of IFRS regulations on local accounting practices. Originality/value –In this paper, researchers use clear technology to measure green technology that has not been used by previous researchers.

Abstract: Music genre classification is a challenging task in the field of music information retrieval due to the overlapping characteristics of certain genres and the variability in audio quality. Several techniques have been developed to accurately classify music genre. However, these techniques have not been adequately analysed and compared. Hence, this study investigates the comparative performance of Convolutional Neural Network (CNN), Support Vector Machine (SVM), and Random Forest (RF) in music genre classification. Mel-Frequency Cepstral Coefficients (MFCCs) were extracted from the audio samples using the Librosa library. Next, the three machine learning models - Convolutional Neural Network (CNN), Support Vector Machine (SVM) and Random Forest (RF) - were trained. The CNN model was designed with multiple convolutional and pooling layers, along with dropout for regularization. The SVM model was used to create an optimal hyperplane for classification, while the RF model utilized an ensemble of decision trees. Finally, the models were evaluated and compared using accuracy, precision, recall and F1 score. The results of the evaluation and comparism indicate that CNN achieved 95% accuracy, 93% precision, 92% recall and 91% F-1 score. SVM achieved 93% accuracy, 90% precision, 80% recall and 70% F-1 score while RF achieved 77% accuracy, 77% precision, 72% recall and 60% F-1 score. The result demonstrated that CNN outperformed SVM and RF interms of accuracy, precision, recall, and F-1 score: CNN is thereby recommended for Music Genre Classification, this finding underscore the efficiency of CNN addressing the challenges task in the field of music information retrieval and leading to the advancement of automated music classification system and improve the accessibility and enjoyment of digital music libraries.

Abstract: The rapid development of generative AI is transforming university information literacy education by reshaping how students access and process information. This study systematically reviews 49 research papers published between 2020 and 2024, using the PRISMA framework and thematic analysis to explore the applications, impacts, and pedagogical changes associated with generative AI in the field of information literacy education. Results show that generative AI has a wide range of applications in information literacy education, mainly in student learning support, learner-oriented personalized learning, academic research assistants, academic writing assistance, information literacy skills development, and curriculum design and teaching assistance. Generative AI has promoted students’ information retrieval, evaluation skills and critical thinking, but also brought the challenge that over-reliance on AI may weaken students’ critical thinking and information evaluation skills. Important changes in curriculum design and teaching methods are needed to introduce instruction in prompt engineering and computational thinking. The role of the teacher has shifted from knowledge transmitter to learning facilitator, emphasizing the importance of professional basic knowledge and ethical education. Through the results it is find that Generative AI can significantly enhance student learning outcomes and skills development in university information literacy education. However, its application requires caution and must fully consider potential challenges and risks. Through reasonable curriculum design, innovative teaching methods, and policy support, educators can leverage the advantages of Generative AI to cultivate high-quality talent with critical thinking, innovation, and a sense of moral responsibility. As AI technology continues to develop, information literacy education will usher in more innovations and opportunities, bringing new vitality and possibilities to higher education.

Abstract: In today’s fast-paced digital age, customer service demands have evolved with customers expecting prompt and accurate responses to their inquiries and request. However, traditional customer service methods often struggle to meet these expectations. The rise of artificial intelligence and natural language processing technologies present an opportunity to address these challenges through the development of intelligent chatbot system. This research aimed to develop and train a chatbot system using ManyChat that can promptly, accurately, and effectively address basic customer inquiries and handle simple request. The methodology employed in this research involved a multi stage approach. Firstly, requirement analysis was conducted through stakeholder meetings, user feedback, and scope definition. Next, the design phase utilized user flow mapping to visualize the customer journey from the initial interaction to issue resolution. The chatbot was then developed on ManyChat platform. Following development, testing, and refinement were performed based on feedback. The performance of the chatbot was evaluated after receiving feedback from multiple users using user satisfaction, task completion rate, and ability to resolve customer inquiries. The results of the evaluation reveal that the developed chatbot has a value of 92%, 87%, and 92% for user satisfaction, task completion rate, and ability to resolve customer inquiries respectively. The findings underscore the ManyChat based chatbot efficiency as a reliable means of providing prompt and accurate responses to customer inquiries. This research proves how ManyChat can be used to build intelligent and scalable chatbot that can be tailored to specific business requirement, and thereby improving the customer experience. 

Abstract: This study investigate the moderating role of exchange volatility on the relationship between economic factors and the financial performance of multinational corporations (MNCs) in Nigeria. The research adopted an ex-post facto research design, and the secondary data was collected from 2013 to 2022. The sample consisted of twenty chosen from the leading forty MNCs in Nigeria based on data availability, and multiple regression was used to establish the moderating effect of exchange rate volatility on these economic relations. The economic variables analyzed show that ROE is sensitive to the oil price, interest rate, and foreign reserves with an indication that an increased oil price and a low interest rate is an added advantage to its financial performance while low foreign reserves and high exchange rate volatility decrease ROE. Also, exchange rate volatility moderate the effect of these economic factors on ROE as evidenced in this study. Therefore, this study conclude that economic stability such as foreign reserves and exchange rates are relevant determinants of MNCs Financial performance in Nigeria. This study strongly recommends that MNCs adopt various risk management techniques, including currency management strategies, to address issues that threaten their stability. In this respect, the government needs to make efforts to strengthen the foreign reserves and control other economic variables favorable for MNCs. Further, the policies require MNCs in the oil-dependent industries to put in place strategies that may help contain variations in performance in line with the level changes in exchange rate volatility or other economic factors essential in maintaining performance in Nigeria’s volatile economy.

Abstract: The integration of Artificial intelligence (AI) into various sectors, including transportation, has a significant impact on human endeavors, in addition to eco-friendly advantages. One of the most promising areas of AI-powered systems is the manufacture of Autonomous Vehicles (AVs). These self-driving cars, also known as driverless, are intelligent vehicles that can operate without human aid or support. AVs are equipped with sophisticated AI-powered technologies such as sensors, radars, Global Positioning System (GPS), and advanced algorithms that can transmit information and navigate the environment using analyzed data. These driverless cars have the potential of revolutionizing the transport sector by improving efficiency, reducing road accidents, improving flexibility, and decreasing congestion. However, AI in AV applications poses some risks and challenges associated with securing systems from cybersecurity threats and attacks. This paper explores the dangers and difficulties of securing AI systems from cyber threats, highlighting various detection and prevention mechanisms. The ethical and legal implications, including strategies to address these challenges proactively, are also discussed. It is believed that the challenges in the automotive industry can be mitigated through collaboration among stakeholders, manufacturers, researchers, IT professionals, and policymakers by implementing robust security measures, conducting regular vulnerability assessments, and leveraging the expertise of software security specialists. Collaboration between industry and cybersecurity professionals is essential to safeguarding public safety.

Abstract: Urban development often leads to uncoordinated land use patterns as neighborhoods face growing demand for commercial spaces, causing them to lose their original local character. This study examines the conversion of residential properties to commercial uses, focusing on the extent of these changes and their influencing factors. The research utilized a survey design, collecting data through structured questionnaires from 86 commercial property owners/occupants and 45 Estate Surveying and Valuation firms. Descriptive and inferential statistics, such as weighted mean scores, least square regression, the Mann Whitney U test, and factor analysis, were employed to analyze the data. The drift schedules (rise or reduction) in the number of properties changed/modified between 2014 and 2022, showed a significant progression in property modifications through a linear trend analysis using least square regression models, with R² values of 0.1927 and 0.4102. The Mann Whitney U test revealed significant differences in opinions on factors driving property changes: planning regulations (p=0.001), use of complementary infrastructure (p=0.033), traffic jams (p=0.000), desire to maximize profits (p=0.000), commercial space demand (p=0.014), and investment potential (p=0.000). Factor analysis (chi-square=1618.722, p≤0.000) identified four primary components: demand, business, commercial agglomeration and planning regulations; property upgrades and governmental factors; supply, demand and increased economic activities; and accessibility and investment maximization. Recommendations include revising land use strategies to enforce stricter controls aligned with master plans, retrofitting existing properties for enhanced energy efficiency and occupant satisfaction, thereby meeting the Environmental, Social, and Governance (ESG) standards, and; substantially reducing the need for the change of use.

Abstract: Terrorism has become a contemporary issue in Africa and in Nigeria particularly. This paper therefore examined terrorism and counter-terrorism measures in Africa with emphasis on Nigeria. Methodologically, dada used for this study was sourced through the qualitative methods of data collection which relied mainly on secondary sources for data collection. This involved the use of literature materials such as textbooks, journal articles, newspapers, seminar papers (published and unpublished), etc., as well as the internet. Data gathered was sourced content and descriptive analysis. The study established that several counter-terrorism measures have been put in place at both national and continental levels to reduce if not eradicate in totality the spate of terrorism but these measures have not been able to achieve the desired results as terrorism has continued to thrive. The failure of these measures anchors on government insensitivity and/or lack of political will to address issues of unemployment, get rich quick syndrome, failed family values and other national questions like power sharing, uneven development and prebendal politics, proliferation of small arms and light weapons, as well as porous borders. The study recommended that Africa must establish a Continental Terrorism Management Center (CTMC) at continental level, while governments at all levels in Nigeria must as a matter of urgency address socio-economic deprivation and the severe wealth inequality among its people that provides recruitment centers for these terrorist groups.

Abstract: The distribution of Cu2+ between buffered aqueous phases and chloroform solutions of 4,4´-(1E,1E´)-1,1´-(ethane-1,2-diylbis(azan-1-yl1ylidene))bis(5-methyl-2-phenyl-2,3-dihydro-1H-pyrazol-3-ol) (H2BuEtP) alone and in the presence of 1-(3-hydroxy-5-methyl-2-phenyl-2,3-dihydro-1H-pyrazol-4-yl) butan-1-one(HBuP) was investigated using solvent-solvent extraction. 200 mgL-1 Cu2+ was used for the study with an equilibration time of 60 minutes. Concentration of Cu2+ in aqueous phases after equilibration was determined with AAS and calculated by difference between Copper concentration in the aqueous phases and the organic phases, and distribution ratios(D) and percentage extractions(%E) were determined. Slope analysis from plots of log D against buffers pHs, ligands concentrations and metal concentrations were used to propose distribution reaction equations and extracted Cu2+ complexes as Cu(HBuEtP.X)(o) for ligand alone and Cu(HBuEtP.BuP)(o) in the presence of HBuP. The extraction constant log Kex, obtained for H2BuEtP (-5.11±0.7) was greater than that for H2BuEtP/HBuP (-12.94±1.26) which indicated HBuP did not exert any synergic effects in the distribution of Cu2+, even though partition coefficient log D for H2BuEtP/HBuP of 2.03 ± 0.81 was > 0.97 ± 0.62 for H2BuEtP. Comparing results with those of other studies, showed carbon chain length of structurally related ligands effects on metal ions distribution is dependent on the particular metal ion. The ligand H2BuEtP was a better extractant for Cu2+ than Ni2+ and Fe2+ only as the results for Pb2+, UO2+ and Cd2+ were better based on log Kex values.

Abstract: This study probes into the effectiveness and challenges of Gnomio, an eLearning platform, in enhancing TVET programs under a hybrid education delivery framework. It was conducted at School for Academics and Industrial Technology, Inc., which is a TESDA-accredited training and assessment center located in Rosario, Batangas, Philippines, with a mixed-method approach, involving 163 participants that were students and instructors. Findings show that 91.4 percent of respondents considered Gnomio "very effective", while 8.6 percent thought it "effective." Features most used included course management at 58.3%, followed by assessment tools with 38.7%, and the two least used features were collaboration at 1.8% and resource sharing 1.2%. Connectivity problems, reported by 91.4%, was the commonly recorded problem, followed by lack of training on how to use the platform at 8.6%. The effect-size analysis showed no significant difference in perception between students and instructors, indicating a generally positive reception. Descriptive statistics showed strong agreement on Gnomio's effectiveness in engagement, feedback, and accessibility but varied responses regarding motivation and satisfaction. The study calls for policy reforms in enhancing technical reliability, expanding training programs, and improving feature collaboration with a view to furthering Gnomio's potential impact on TVET hybrid learning while ensuring quality vocational education.

Abstract: Car owners face the challenge of finding convenient and efficient car wash services in San Vicente, Alaminos City. Among them, Shee Auto Polish & Ceramic Coating stands out, but its manual appointment booking process often leaves customers frustrated with long wait times and scheduling conflicts. This research introduces a system architecture for a digital platform facilitating car wash appointments at Shee Auto Polish & Ceramic Coating. It aims to address inefficiencies observed in the conventional manual booking method. A solid foundation for comprehending the background and complications behind the development of the car wash booking system is provided by the study’s framework, which is enhanced by the incorporation of ideas from relevant literature. To obtain data from respondents, the researchers utilized a range of data collection methods, such as questionnaires for surveys, in-person interviews, internet searches, document analysis, and observation. The system facilitates customer and employee workflow, improves overall happiness and convenience for the shop and for the community, and offers a user-friendly interface in the system for better usage, effective data management, and improved service delivery for the car wash facility. As Shee Auto Polish & Ceramic Coating anticipates more vehicles, developing an efficient online booking system becomes crucial for improving customer service and operational efficiency.

Abstract— Noise pollution has become a significant environmental issue, with the frequent use of vehicle horns being a major factor. This study presents a creative approach titled “IR Communication-Based Alternative Horn Signaling System for Vehicles to Reduce Sound Pollution,” which aims to reduce horn usage in road traffic, particularly on highways, by implementing infrared (IR) communication technology. The objective of this project is to identify effective alternatives that allow vehicles to communicate without relying on horn usage, thereby decreasing overall sound pollution. Specifically, the proposed system is intended to enhance communication between vehicles on highways and where traditional horn signaling is inappropriate, such as near schools and hospitals. In urban areas, drivers typically use horns within 10 to 30 meters, with a range of 30 to 50 meters in other situations. This system achieves 100% accuracy within 20 meters and at least 92% accuracy up to 50 meters. The system uses two ATtiny85 microcontrollers: Microcontroller-1 for transmission and Microcontroller-2 for reception. Microcontroller-1 connects to a push button and three IR LEDs, while Microcontroller-2 connects to an IR receiver (TSOP-1838 sensor) and a combination of LED and buzzer. By allowing vehicles to communicate without audible horns, this project seeks to reduce noise pollution, especially in low-traffic areas significantly. The findings indicate that this IR communication system could effectively enhance vehicle communication while also contributing to lower noise pollution levels.

Abstract: Human immunodeficiency virus (HIV), the primary cause of Acquired Immunodeficiency Syndrome (AIDS), is responsible for millions of deaths worldwide. As of 2022, Nigeria has a prevalence rate of 1.4%, with approximately 1.9 million individuals infected, contributing to about two-thirds of the deaths attributable to this illness in sub-Saharan Africa. Micronutrient deficiency is a common issue among individuals living with HIV, exacerbating immune suppression, negatively impacting prognosis, and accelerating the progression of the infection. Therefore, this research aimed to investigate the effects of various doses of selenium and zinc supplements on the weight and immune function of HIV-infected individuals through weight and CD4 monitoring, with the goal of providing a solution for improved management of HIV. A total of 30 subjects (25 HIV-infected individuals and 5 healthy controls) were selected and divided into six groups, with five individuals per group; groups 1 and 6 served as negative and positive controls, respectively. Different doses of selenium and zinc, or combinations thereof, were administered to the groups for 12 weeks while assessing the outcomes through weight measurements and CD4 count analysis at 28-day intervals. The generated data were analyzed using two-way ANOVA. The results demonstrated a significant difference in mean weight and CD4 counts across the different groups (p-value < 0.05). This indicates that selenium and zinc supplements are viable options for enhancing antiretroviral therapy (ART) in the treatment of HIV..

Abstract: The software development landscape has undergone a significant transformation, driven by the rapid evolution of cloud computing and the increasing adoption of DevOps practices. In this context, the integration of Artificial Intelligence into cloud-based CI/CD (Continuous Integration/Continuous Deployment) pipelines has the potential to revolutionize the way software is developed, deployed, and maintained. This research paper explores the impact of AI-driven workflows on enhancing efficiency and productivity in the CI/CD process. The paper examines the challenges and opportunities presented by the intersection of AI and DevOps, drawing insights from real-world case studies and industry trends. It investigates how AI technologies, such as machine learning and natural language processing, can optimize various stages of the software development lifecycle, including requirements engineering, code generation, testing, and deployment. Furthermore, the paper discusses the ethical implications and potential risks associated with the integration of AI in software development, addressing concerns such as bias, transparency, and the need for human oversight. By analyzing the current state of AI-driven cloud workflows and their impact on CI/CD pipelines, this research paper aims to provide valuable insights for software development teams, DevOps practitioners, and decision-makers. The findings of this study suggest that the strategic integration of AI-powered technologies can enhance the efficiency, agility, and reliability of software delivery, ultimately enabling organizations to stay competitive in the rapidly evolving digital landscape. [1]

Abstract: This study aims to analyze the digital policing of the Dittipidsiber in handling online gambling. The focus of this study includes the development of rampant online gambling in Indonesia, the organizational resources of the Dittipidsiber in handling online gambling at this time, and the policing of the Dittipidsiber in handling online gambling. The paradigm of this study is the constructivism paradigm. The research approach used by the author is a qualitative research approach. The method used by the researcher is a descriptive analysis method. The results of the study show that 1) Online gambling is a form of legal violation that needs to be eradicated. Driving factors can come from the individual himself or from the environment; 2) The condition of the resources of the Dittipidsiber Unit is still limited, especially in terms of human resources, facilities and infrastructure and budget. Meanwhile, the ICT condition of the Directorate of Cyber ​​Crimes of the National Police Criminal Investigation Unit in handling online gambling is "quite adequate" for now, although ongoing development and maintenance are still needed; 3) The policing of the Dittipidsiber in handling online gambling has basically been implemented through preemptive, preventive, and repressive strategies. Dittipidsiber in handling online gambling has also implemented communication, coordination and collaboration, both internally and externally, although the Dittipidsiber still tends to prioritize repressive strategies. The conclusion of the study show that digital policing of the Dittipidsiber in handling online gambling still need to be improved.

Abstract: This study examined the influence of repayment period on loan package choices among SACCO members in Kakamega Central Sub-County, Kenya, addressing the challenge where members struggle to access loans despite substantial deposits due to repayment constraints. The objective was to determine how repayment period influences loan package selection, guided by Agency Theory which explains the relationship between SACCO management and members in setting loan terms. Using a descriptive survey design, data was collected from 374 respondents selected from a population of 9,758 SACCO members through structured questionnaires out of which 346 questionnaires were filled and returned. Regression analysis revealed that repayment period significantly influences loan package choice (R²=0.232, β=0.482, p=0.004), explaining 23.2% of variation in selection. The study concluded that members favor loan packages with repayment terms aligned to their income patterns and loan purposes, with 44.5% preferring development loans offering 36-48 month terms. Key recommendations include implementing flexible repayment options, aligning periods with member income patterns, and considering loan purpose when setting terms.

Abstract: This study investigates microbial behavior and ecotoxicological impacts in thermal radiation magnetohydrodynamic (MHD) Casson fluid flow over a stretching sheet. The study incorporates non-uniform source effects, highlighting its relevance to biological systems and industrial applications. A hybrid approach combining multilinear regression and streamline analysis is utilized to explore the relationship between key system parameters—including magnetic field strength, thermal radiation, and non-uniform sources—and microbial behavior. Numerical simulations analyze temperature, velocity, and concentration fields to assess their impact on microbial growth and distribution. The findings indicate significant influences of thermal radiation, magnetic fields, and non-uniform sources on microbial behavior, emphasizing the importance of ecotoxicological considerations. This study introduces a novel integration of multilinear regression and streamline analysis to enhance pollutant transport modeling, providing valuable insights for environmental and industrial applications.

Abstract: The rapid digital transformation has triggered an explosion in data generation, with its core impact on the marketing landscape. Big data, with huge volumes, speed, and variety, is thus a significant field of opportunities and challenges for marketers seeking to unravel actionable insights. Traditional approaches to data processing are only inefficient and unable to manage such scale and complexity of data. However, with the advent of AI, quite a few advanced tools can handle big data with greater efficiency, thus enabling better consumer understanding, personalization of marketing strategies, and quick decision-making. It has revolutionized marketing, where systems can now analyze big datasets, recognize patterns, and predict customer behaviors. From descriptive analytics, the shift toward predictive and prescriptive has empowered businesses to optimize campaigns toward enhanced customer experiences. This integration of AI means it can be done instantly, enabling real-time response and fostering more relevant consumer engagement. This review delivers a critical outlook on the current trends in AI, their application to marketing, and the challenges businesses face in implementing these new technologies. Ethical issues around data privacy, transparency, and bias in AI models are discussed. The paper highlights future research directions, including federated learning, quantum computing, and multimodal AI, which hold great promise for even further transformation of the marketing domain.

The education system thrives on the dedication and passion of its teachers; however, teaching in a remote school setting presents unique challenges that significantly impact teachers' professional experiences and well-being. This qualitative-phenomenological study explored the lived experiences of teachers teaching in remote schools, focusing on their challenges, coping strategies, and insights. The study was supported by Bandura's Self-Efficacy Theory (1997), emphasizing the critical role of teachers' confidence in overcoming challenges and improving student outcomes, particularly in remote areas. The participants of this study were six (6) teachers from a remote school setting selected through a purposive sampling technique. The research utilized semi-structured interviews with open-ended questions to gather rich data. The results of the study revealed three (3) major themes in the challenges experienced by the participants; these include the inconvenience of traveling to and from, limited resources available, and difficulty in adapting to the new environment. For their coping strategies, teachers highlighted accepting the challenges, being flexible and adaptable, and building relationships with students, colleagues, and the school community as keyways to manage their circumstances. Finally, for the insights, teachers noted that their experiences developed patience and adaptability, called for the localization of teachers’ assignments, and taught them to be appreciative of the situation. The findings provided a deeper understanding of the unique struggles and growth of teachers in remote areas, emphasizing the need for systemic support to address their challenges and improve their working conditions. Moreover, understanding their experiences can enhance their professional satisfaction and overall well-being, ultimately benefiting both their personal and professional lives.

Abstract: Stock price prediction is a critical task in financial markets, often complicated by the challenges of modeling highly volatile, non-linear, and dynamic time series data. This study evaluates the performance of two prominent forecasting models: Long Short-Term Memory (LSTM) networks, known for their ability to capture long-term dependencies and non-linear patterns, and the Auto-Regressive Integrated Moving Average (ARIMA), a traditional statistical model adept at linear trend modeling. Historical stock price data from the Nigerian Exchange Limited (NGX) was utilized. Both models were implemented in a Python environment, and their predictive accuracy was assessed using performance metrics, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and R-squared (R²). Additionally, the Diebold-Mariano (DM) test was employed to statistically compare the models’ predictive accuracies. This study highlights the potential of LSTM models for robust stock price forecasting and provides valuable insights for selecting predictive models based on data complexity and market dynamics.

Abstract: The rapid growth of Artificial Intelligence (AI) has increasefd the demand for scalable, efficient, and cost-effective computational infrastructure. Traditional on-premise systems face limitations in scalability, resource allocation, and cost efficiency, making cloud computing a preferred solution. This paper examines cloud-native architectures, including containerization, Kubernetes orchestration, serverless computing, and microservices, as key enablers of AI scalability. Modern approaches for optimizing AI models involve using quantization and pruning and knowledge distillation approaches to make them more efficient without sacrificing their accuracy levels. The paper investigates workload distribution methods like federated learning together with distributed training plus adaptive AI scaling for improving resource efficiency and lowering response times. The implementation continues to face difficulties concerning expense control and latency reduction and scheduling resources efficiently while ensuring security standards. The research presents three possible solutions namely automated AI scaling, edge-cloud integration and provisioning with cost intelligent management systems to overcome current limitations. This examination features a study of present-day trends which consist of AI-native cloud orchestration along with AutoML-based optimization and quantum computing applications for the enhancement of AI scaling capabilities. This research provides comprehensive insights about cloud-based AI scalability which helps researchers as well as practitioners improve their deployment and optimization capabilities of high-performance AI systems.

Abstract: The aim of this study was to determine the effects of demographic variables such as age, gender, organization size and organization ownership on the sources of managerial guidance most often used by managers from Nigeria and UK. The guidance sources included formal rules, unwritten rules, subordinates, specialists, co-workers, superior, own experience and widespread beliefs as to what is right. Data were collected from 288 Nigerian and 141 UK managers. UK managers reported relying most frequently on their own experience, Nigerian managers reported relying most strongly on formal rules. Hierarchical regressions showed that reliance on formal rules, on superiors, on unwritten rules and on subordinates were significantly related to demographic predictors. However, the demographic predictors of reliance on these sources differed between the two countries and these differences are interpreted in terms of differences in national culture.

Abstract: Posting of advertisement graphical bills constitutes one of the biggest threats to man and his environment as it now appears and as we live in an age of growing sensitivity to environmental cleanliness. Pollution of the Nigerian cities through advertisement bills assumes the general term of outdoor advertising ranging from hand-printed signs, posters, painted bulletins and spectaculars. The concept of the urban and environmental graphics in the city aesthetic planning system has gained currency in many advanced countries. Nigerian signs and billboards can be referred to as environmental pollution because they create problems, and affect the urbanization planning in Nigerian cities. Also, the concepts equally see the objects in the streets as making up the city as figure and decorations not mere junks. As a result, urban and environmental graphics embraces city planning, which lacks care of all spheres - physical, aesthetics, transportation, waste disposal, erection of billboards, posters and other constructions that could enhance the beauty as well as harm the image of the city. The study area is Lokoja and its environs. Random sampling technique was adopted to ensure that each unit of the population is well represented in order to get the necessary information from the samples. The findings emphasize the importance of strict control and market regulations in posting of advertisement bill in built environment and eradication of pollution of the environment.

This study examines the sectoral contributions of oil and non-oil tax revenues in Nigeria and their implications for economic diversification. Given Nigeria’s historical dependence on oil tax revenues, the volatility of global oil markets has raised concerns about fiscal sustainability. Using Generalized Least Squares (GLS) regression, Vector Autoregression (VAR) models, and Cointegration Analysis, this research evaluates the relationship between tax revenue composition and economic diversification over the period 2010–2021. The findings indicate that non-oil tax revenues have a stronger and more stable impact on economic growth and diversification than oil tax revenues. The results support the Resource Curse Theory and Fiscal Neutrality Theory by demonstrating that an overreliance on oil taxation creates structural inefficiencies while a diversified tax structure enhances fiscal stability. The study highlights challenges such as weak tax compliance, inefficient tax collection, and a narrow tax base, emphasizing the need for policy reforms to enhance non-oil tax revenue mobilization. Recommendations include broadening the tax base, improving tax administration, investing in non-oil sectors, and strengthening public-private partnerships to foster sustainable economic growth. The research provides valuable insights for policymakers and economic planners seeking to optimize Nigeria’s tax revenue structure and ensure long-term economic resilience.

This paper presents an original reaction kinetics model as a tool for estimating the carbon yield in hydrothermal carbonization (HTC) of biomass. The kinetics model was developed in MATLAB. The reaction pathways described through a model, in which biomass is converted into solid, liquid and gaseous products. Runge-Kutta method was used to solve the 4 equations derived from the reaction suggestion, through the estimation of 6 Arrhenius kinetics parameters (k1, k2, k3, k4, k5, and k6).  The HTC reaction pathway was described through a lumped model, in which biomass is converted into three phase, residual solid, liquid and gaseous products. By choosing the values of k1 and others k values and modelling predictions are in the good of the figure pattern. The k values gave good pattern of figure 3 were k1 = 0.3, k2=0.1, k3=0.3, k4=0.1, k5=0.2, and k6=0.1. They resulted that the biomass conversion was very high about of 90%, carbon content of 39%, liquid content of 24, and gaseous content of 30%.

The numerous activities carried out at Wawanrafi dam and farm site requires that a study of the aftermath of such activities be investigated. This study is aimed at determining the levels of heavy metals in some selected commonly consumed fruits (mango and guava) grown in the vicinity of the dam and in Kazaure market place. The levels of cadmium, copper, chromium and iron were determined in the edible and non-edible parts of mango, (Mangifera indica) and guava (Psidium guavaja) grown in Wawanrafi village and Kazaure market, Kazaure, Jigawa state Nigeria and in the soil where they are cultivated and the in the water used in irrigating the crops using atomic absorption spectrophotometric analysis, AAS techniques. The results revealed the heavy metal burden such as Cd, Cr, Cu and Fe in mango parts, seed, skin and pulp are in the range 0.2466 – 0.7500µg/g; 0.1660 – 0.9000µg/g and 0.4333 – 2.2170µg/g respectively. The ranges of concentration of heavy metals in various parts of guava such as seed, skin and pulp are 0.2570 – 1.5300 µg/g; 0.2570 – 1.133µg/g and 0.3600 – 1.1770µg/g. The findings reported the following ranges for Kazaure market fruits samples parts viz a viz seed, skin and fleshy pulp: Kazaure market guava fruit sample, KMGFS, 0.0827 – 0.8340µg/g, 0.1960 – 0.6410µg/g & 0.2670 – 1,017µg/g and Kazaure market mango fruit sample, KMMFS, 0.0836 – 0.611µg/g, 0.0450 – 0.7460µg/g & 0.0853 – 0.726µg/g. The results also show that on the whole plant basis, guava concentrated more of Cd and Cu than mango, while mango has higher levels of Cr and Fe than guava. Chromium is least detected in the pulp of guava. It could be concluded that mango and guava obtained in Wawanrafi village contained the heavy metals in variable proportions due to different activities such as fertilizer application, irrigation with dam water, coupled with other human activities like fishing, washing of clothes, vehicles and motorcycles, tourism. The heavy metal levels were lower than the WHO maximum limit, hence the fruit crops are considered safe for consumption. 

Abstract: Two macroeconomic policy options toward achieving a sustainable inflation threshold as well as a stable demand for money function which are capable of fostering pragmatic effort of achieving other macroeconomic goals within the West Africa Monetary Zone (WAMZ) countries, but not much has been achieved. This study examines inflation threshold and demand for money function in the West Africa monetary zone countries, providing insight into attaining the threshold level of inflation that is considered optimally reasonable for maintaining a stable demand for money function. The study employs a quantitative data analysis technique. Data used for this study are annual time series data spanning from 1980-2023 covering the West Africa monetary zone countries. The study employed the autoregressive distributed lag (ARDL) bound testing model to test for time series properties of the relevant data and the vector autoregressive (VAR) model to estimate the threshold level of inflation. Furthermore, error correction modeling (ECM) approach was explored to identify both the short-run and long-run impact of inflation threshold among other major determinants of demand for money function in selected countries in the West African monetary zone. The findings indicate that the expected specified demand for money function was stable and well behaved in the West African Monetary Zones countries. The result further showed that monetary expansion can be both inflationary and deflationary, depending on the country’s economic structure and policy environment.

Abstract: The demand for customer-focused manufacturing calls for more user-friendly tools to make workflow as smooth as possible. One of the lean tool options integrated into the automobile industry to tackle problems associated with product quality, brand appeal, and waste elimination at every stage of production processes is the Value Stream Mapping (VSM). Though manufacturers adopted several lean tools to minimise cost and reduce waste, this study focused on the roles of Value Stream Mapping (VSM) on the operational efficiency of Innoson Vehicle Manufacturing Company of Nigeria. A descriptive survey design was used to structure and organise this study. The convenience sampling technique was used to select 110 management and supervisory staff from the 1,800 employees of Innoson Vehicle Manufacturing Company of Nigeria. Data gathered were analyzed using the Ordinal Logistic Regression (OLR) model in the Statistical Package for Social Sciences (SPSS) Version 21. The null hypothesis that VSM as a Continuous Improvement (CI) tool does not enhance the firm's Operational Efficiency (OE) was rejected at a LOGCI of 872.31 and p-value of .001. The hypothesis, that waste elimination through VSM does not influence operational efficiency, was also dismissed. The findings revealed that VSM performance is demonstrated when teamwork in an assembling procedure, interacts with the production system and watches how production evolves from a starting point (inputs) to an end item (output). It was concluded that to achieve operational efficiency, businesses must minimise waste of processes, raw materials, time and space. VSM helped the managers to visualise more than just the single-process level, enabling the managers to view job flow across all processes. We recommended VSM to the manufacturing and the service industry for the implementation of plans and actions.

Abstract: With highly rapid progress in artificial intelligence (AI) technology, AI has evolved dramatically in nearly all aspects of human life, especially private life. This article explores instances of AI as displacement to/ augmentation of human connections, concerning the ramifications of AI-based companions, Robots Daily paper Knowledge Date: December 2023 Today Date: 26 Jul 2024 Rephrase the following sentence. Use the same language as the original sentence. With AI becoming more ordinary in ordinary life, the question is: Can AI truly replicate the tone and subtlety of human connections, or is it only an addition? New AI technologies like chatbots, virtual assistants, and social robots are created to hold a user in a conversation and to give emotional comfort. These advancements indicate that AI can complete information omissions in social interactions when people feel lonely or anxious about being socialized. For instance, AI-powered mental health apps can generate therapeutic conversation as a substitute of companionship. Though these technologies can imitate human contact, they are often compelled to overlook the emotional attunement and comprehend that element of human relationships. The intricacies of human emotions—sourced in shared experiences, feelings, and interpersonal relationships—remain tough for AI to grasp and mimic perfectly. Even in the realm of dating and social connectors, the introduction of AI says something about its future: its potential on human relationships. Sophisticated algorithms are used to screen customer preferences and activities for making connections, simplifying the look for romantic partners. Although these programs can enrich communication by linking people, they also partially end up as the credibility of relationships set up via such software. Users are likely to interact with AI-produced profiles or personas that do not have the emotional or psychological form present in one genuine human interaction. The psychological consequences of having AI as companionship are enormous. At the core of this is that meaningful human relationships are crucial for emotional wellness and lowering feelings of loneliness and general life contentment. Suppose people add AI (Artificial Intelligence) as the primary option for companionship to the picture. In that case, there is a risk of alienating from authentic relationships even more, deepening the feelings of loneliness. The love feeling comes from genuine human interactions, unlike AI, which is based on algorithms and information instead of genuine feeling and true individual connection. Besides, the ethical issues also enter the picture when AI becomes more affirmative about personal relationships. The idea of psycho-emotional influence using AI raises issues about consent and authenticity of emotional perception. AsDesde que la sociedad transitable de éstas complejidades, es necesario definir marcos que pongan en el centro la conexión y el bienestar humano. This encompasses promoting an understanding of the boundaries of AI when it comes to mimicking human emotions and stimulating the creation of technologies that complement, not detract from, interpersonal relationships. AI can increase human relationships by providing assistance and companionship with humanity, but it can not replace human relationships' depth, profundity, and complexity. The subtleties of emotional resonance, connection and shared experience, and emotional are considered on the strength of what the facts suggest to be a substantial evidential base. As AI becomes more pertinent in our lives, we must be conscious of reaching an equilibrium where the human part of how one communicates is kept while we benefit from what AI can afford. The research into artificial love shows the importance of ongoing conversation around the place of technology in our social lives and the inseparability between the reality of our online and offline lives in that ongoing conversation.

Abstract: Software systems requiring complex testing benefit greatly from both exploratory testing and edge-case analysis because these methods reveal defects that automated testing would miss. The growing use of automation to quicken testing operations requires organizations to achieve proper equilibrium between automated tools and skilled human labor. This document examines how machine testing benefits from human involvement to perform exploratory testing and identify any hidden edge-case situations. The advantage of automation tools lies in their ability to deliver both speed and uniformity but they struggle to replicate essential human capabilities which help recognize hidden issues in unusual circumstances (Baresi et al., 2022). Human testers have a strong ability to detect stealthy defects yet their testing capabilities face challenges in achieving complete scalability and coverage (Smith & Zhang, 2021). This paper evaluates the difficulties and advantages of uniting automated testing solutions with people-driven exploratory testing during edge-case scenario analysis. The combination of automated testing and human tester intervention represents the best approach according to current industry practices since automation performs repetitive tasks and human testers handle critical thinking tasks and pattern recognition with defect discovery responsibilities (Carver et al., 2023). Our research develops a framework to merge automated and manual testing methods and it examines AI exploratory testing solutions and human-assisted automated testing environments for future development. Organizations need to completely evaluate their testing methods so they can combine automated functions with human cognition properly for enhanced testing methodology (Jain & Singh, 2020).

Abstract: Blue Economy is a recently coined concept in resource planning. The concept encompasses all economic activities that depend on the sea, such as tourism, maritime transport, energy and fishing. Blue economy facilitates the sustainable growth of a nation’s economy; as the oceans and seas are engines of the global economy with great potential for livelihoods enhancement, and a catalyst for economic growth and development. The study unravels appropriate mechanism for poverty alleviation in Nigeria by unlocking the opportunities offered by the blue economy. The study conceptually reviewed existing literature as a basis for obtaining an in-depth understanding of how blue economy potentials can provide alternative livelihoods asides generating employment opportunities in fisheries, tourism, and other ocean based industries that could boost the wellbeing of households and their income generating capability in Nigeria. It is revealed that blue economy has far-reaching potential for expanding economic activities and poverty reduction if the economic, social, technological, cultural and environmental challenges fettering the development of the sector are properly addressed. Consequently, the study recommends strong political will and robust institutions for effective development and implementation of blue economy policies in order to bolster livelihood activities in Nigeria. The need to establish thorough policy coordination and stakeholder engagement to provide formidable structure for the development of blue economy in Nigeria is suggested.  

Abstract: As environmental concerns continue to grow, industries are compelled to implement sustainable supply chain processes. Green supply chain management (GSCM) has become a key strategy for reducing the negative effects of supply chains on the environment. It includes anything from energy-efficient logistics to environmentally friendly product design. Simultaneously, artificial intelligence (AI) is transforming supply chain management through improved decision-making, optimization, and efficiency capabilities. Businesses have a great chance to achieve sustainability objectives while preserving operational performance at the nexus of AI and GSCM. This study aims to investigate how AI applications are currently used in green supply chain management, identify possible advantages and difficulties, and offer predictions about recent advances in the field. The results show that supply chain sustainability can be significantly increased by using AI applications. More precise demand forecasting and improved waste management techniques that reduce resource use are two major advantages. AI such as machine learning and predictive analytics, enable businesses to automate labour-intensive procedures and make smart judgements instantly while monitoring environmental performance. In conclusion, AI has a lot of potential to promote environmentally friendly supply chain management approaches that also improve operational effectiveness.

Abstract: The study examines the challenges associated with the application of circular economy principles for construction and demolition waste management in South East Nigeria. A survey research design was adopted, utilizing structured questionnaires to collect data from building construction professionals involved in ongoing projects across Anambra, Enugu, Abia, Ebonyi, and Imo States. The target population consisted of 1,653 key stakeholders, including 333 clients, 894 contractors, and 426 consultants engaged in public projects. A purposive sampling technique was employed to select a sample size of 322 participants, comprising 131 contractors, 40 clients, and 151 consultants proportionally distributed across the five states. The study's findings, assessed using a mean decision rule of 2.50, reveal significant barriers to the effective adoption of circular economy principles in the study area. Key challenges include the limited availability of infrastructure and technology necessary for sorting, recycling, and reuse of construction and demolition waste, which impedes resource recovery within a circular economy framework. A lack of collaboration and coordination among stakeholders disrupts the seamless flow of materials and information essential for implementing circular economy initiatives. Furthermore, the insufficient integration of circular economy principles into current waste management practices highlights a gap between theoretical frameworks and practical applications. Resistance to change within the construction industry, stemming from entrenched practices and concerns about cost or feasibility, further exacerbates the issue. To advance the adoption of circular economy practices, the study recommends the provision of adequate infrastructure and technology to facilitate efficient waste sorting, recycling, and reuse. Addressing these barriers is critical for optimizing resource efficiency, minimizing environmental impact, and promoting sustainability in construction waste management.