FUPRE Journal of Scientific and Industrial Research (FJSIR)

Interpretation of “ERT” Data Using ADMT Method from Industrial Waste Migration into Groundwater Depth in an ‘X’ Location

2025-10-01T23:08:19+00:00

This study investigates the subsurface impact of industrial waste migration into
groundwater systems using Electrical Resistivity Tomography (ERT) around the
Choba Campus, University of Port Harcourt. The area lies within the highly
porous and permeable Benin Formation of the Niger Delta, making it susceptible
to leachate infiltration. Industrial activities in this region produce poorly managed
wastes, which percolate through sandy soils and unconfined aquifers, threatening
potable groundwater sources. Using Apparent Diffusion Magnetic Technology
(ADMT), a digital resistivity technique, multiple 2D resistivity profiles were
acquired to identify zones of contamination. The tomographic sections revealed
low resistivity values (<100Ùm), especially within the upper 30 meters of the
subsurface consistent with leachate saturation. These anomalies were widespread,
suggesting both vertical and lateral migration of contaminants. The
hydrogeological setting characterized by sandy loam topsoil, fine-to-medium
sands, shallow water tables (1.5–7m), and poor drainage further enhances leachate
transport. The resistivity data were corroborated by geological information,
showing alignment with porous sandy layers typical of the Benin Formation. The
spatial extent and depth of anomalies underline the vulnerability of groundwater
in the area. This study confirms ERT’s efficacy as a non-invasive environmental
monitoring tool for delineating contaminant plumes. The findings emphasize the
need for proactive groundwater protection, proper industrial waste management,
and potential remediation strategies in industrial-residential interface zones. ERT
proves valuable not only for detecting existing pollution but also for guiding future
land-use and environmental policy in developing regions facing industrialization
pressures.

Heavy Metal Contamination of Vegetation by Sawmill Activities along Udu River

2025-10-01T23:08:21+00:00

Water bodies are acknowledged dumping ground for both industrial, manufacturing and domestic activities along coastlines. The Owhase creek, a typical example of a stream receiving wood wastes from sawmill industries is a source of water and livelihood to indigenous community and also as a routes to neighbouring communities. Hence, this study is carried out to review the “Heavy Metal Contamination of Vegetation by Sawmill Activities along Udu River”. While fish sampling was carried out twice, one during the wet season (July) and another one during the dry season (December), both samples were tested for heavy metal contamination using the APHA 3111A test method exploring the single acid wet oxidation method to extract the heavy metals. Results from this study revealed that sawmill activities has significant environmental impact on the Owhase creeks and its environment. The values for heavy metals investigated in this study such as Iron (Fe) and Cadmium (Cd) values ranging from 1.921 to 12.412 mg/l and 0.044 to 0.147 mg/l respectively and they are above the national regulatory limits of <1 mg/l and 0.005 mg/l respectively (NSDWQ, 2015). Also, Iron (Fe) values across kolokolo (Synodontis rukwaensis) and bounds (Cirrhinus reba) internal organs were higher than the permissible limits. Zinc (Zn) values was higher than the permissible limits in the intestine of the Kolokolo (Synodontis rukwaensis) and the gills and intestines of the bounds (Cirrhinus reba). Cadmium (Cd) values were higher than the permissible limits in the gills, intestines, and bones of the bounds (Cirrhinus reba) fish species only. It was recommended that sawdust from sawmill should not be dumped into the waterways to avoid further pollution of the waters. The level of heavy metal contamination in the water body should be monitored regularly. Such data should be used for the assessment of health risk in the habitat.

Inertial Residual Projection Method (IRPM) for Approximating Solutions of Variational Inequality Problems

2025-10-01T23:08:22+00:00

This study proposes a new inertial residual projection method (IRPM) with or
without Halpern update for solving monotone variational inequality problems
(VIPs) in real Hilbert spaces. Existing explicit projection methods, including those
introduced by Noor et al., 2000a, 2000b, are limited by weak convergence
guarantees, multiple projection steps per iteration, and fixed step-size
dependence—factors that hinder their efficiency, robustness, and scalability. To
address these limitations, the proposed IRPM-H method integrates an inertial
extrapolation step for acceleration, Halpern-type anchoring for strong
convergence, and a residual-based adaptive step-size strategy that eliminates the
need for prior knowledge of Lipschitz constants. The algorithm is designed to solve
VIPs involving monotone operators such as linear mappings with positive
semidefinite matrices and gradients of convex functions. Under standard
monotonicity and continuity assumptions, we prove that the sequence generated
by the IRPM-H method converges strongly to a solution of the variational
inequality, which also satisfies the fixed-point formulation. Numerical illustrations
were given to justify the theretical assertons and to demonstrate the effectiveness
of the proposed models. The results shows that our model compete favourably with
other existing models cited in the literature.

Green Synthesis and Evaluation of Antimicrobial Soap from Bio-Oil of Water Hyacinth and ZnO Nanoparticles Derived from Waste Plantain Peel

2025-10-01T23:08:23+00:00

This study explores a sustainable approach to soap production through the green
synthesis of Antimicrobial soap using bio-oil extracted from Eichhornia crassipes
(water hyacinth) and zinc oxide (ZnO) nanoparticles biosynthesised from waste
Musa paradisiaca (plantain peel). Water hyacinth, an invasive aquatic plant, and
plantain peels, an agro-waste, were utilised to address environmental waste issues
while enhancing public health through eco-friendly hygiene innovations. Bio-oil
was obtained via pyrolysis, while ZnO nanoparticles were synthesised using
aqueous plantain peel extract as a reducing and stabilising agent. Phytochemical
screening revealed the presence of antimicrobial constituents such as flavonoids,
tannins, and saponins in both materials. Soap formulations containing 0%, 5%,
10%, and 15% ZnO nanoparticles were produced and evaluated for pH, foaming
ability, stability, and antimicrobial efficacy against Escherichia coli,
Staphylococcus aureus, and Pseudomonas aeruginosa. The 10% ZnO soap
demonstrated optimal balance with acceptable foaming, skin-friendly pH, and
significant inhibition zones (up to 26 mm), suggesting a synergistic antimicrobial
effect of phytochemicals and nanoparticles. The soap also maintained structural
stability over six weeks. This work exemplifies a circular economy model by
transforming environmental and agro-waste into high-value antimicrobial
products, offering potential for commercialisation and application in underserved
communities. The study contributes to sustainable development goals related to
health, sanitation, and responsible consumption. It sets a foundation for largerscale
green product development.

Technological Innovations in Teaching and Learning in Nigeria: A Review of Progress, Engagement Strategies, and Challenges

2025-10-01T23:08:24+00:00

This study provides an in-depth review of the engagement of technological
innovations in teaching and learning within the Nigerian educational landscape
from 2010 till 2025. It examines the key technological tools and platforms adopted,
focusing on their role in fostering student engagement (behavioral, emotional, and
cognitive). The paper analyzes the evolution of EdTech in Nigeria, influenced by
national policies, socio-economic factors, and global trends, including the
significant impact of the COVID-19 pandemic. While highlighting progress in
areas such as mobile learning, e-learning platforms, and the use of social media,
the review also critically assesses the persistent challenges that hinder widespread
and effective integration. These include infrastructural deficits, digital literacy
gaps, policy implementation inconsistencies, and socio-economic disparities.
Drawing on recent literature, this paper discusses opportunities for enhancing
technological engagement and proposes future directions for research, policy, and
practice to create more interactive, inclusive, and effective learning environments
in Nigeria.

Faster Fixed Point Iterative Scheme for Contraction Mappings

2025-10-01T23:08:25+00:00

In this paper, it is our aim to construct and study a new iteration scheme for the
approximation of fixed points of several contraction mappings in the sense of
Berinde. We prove strong convergence result for the suggested scheme under
Lipchitz conditions. In addition, we demonstrate numerical simulations through
tables and graphs displays to show that our suggested innovative scheme converges
faster than many previously introduced iterative schemes for this mapping also in
the sense of Berinde. Furthermore, a stability analysis under perturbation test is
carried out. The results shows that with any given perturbation, the perturbed
sequences also converges to the fixed of the mapping under investigation same as
the unperturbed sequence. Our findings shows the effectiveness of our suggested
scheme and can be used in approximating fixed points of contraction mappings
and its applications in the field of epidemiology, engineering and computer science.

Enhanced Loyalty Management System Wıthın A Dıgıtal Wallet For Seamless Shoppıng Experıence

2025-10-01T23:08:26+00:00

Businesses and organisations are always looking for a way to improve sales and
enhance customer satisfaction and experience, irrespective of the highly volatile
competitive business environment. With the rise of a cashless society and the
involvement of electronic transactions and the risk of carrying cash, which can
result in theft, loss, and stress, the need for a robust system to manage the
customer's experience and evoke loyalty has become paramount. This project,
therefore, aimed at the development of an improved digital wallet and loyalty
management system for a seamless shopping experience using a mobile application
development framework to enhance customer loyalty and satisfaction. The system
was developed using the adaptive software development (ASD) methodology,
which is an improvement of the rapid application development (RAD) that
addresses the internet economy and JavaScript, Typescript, React Native in Visual
Studio Code IDE. The system was deployed and tested with several components
and modules that handle user registration and account creation, loyalty point
generation, transfer and gift card creation, respectively. The system is efficient and
reliable, as it evokes customer loyalty and improves customer satisfaction and
experience.

Empowering the Next Generation of Women Scientists through Quality Science Education

2025-10-01T23:08:27+00:00

Women constitute a significant number of the world’s population and,
consequently, the workforce of any nation, including the science research
community. They therefore make important and transformative contributions also
in national development areas, including scientific innovation, policy formulation,
national planning, and sustainable technological development essential for shaping
a nation's future. However, there exist persistent disparities in the entry, presence,
sustenance and career progression of women in science, technology, engineering,
and mathematics (STEM) fields, limiting the full tapping and realization of the
potential of women thereby emasculating their influence in popularizing and
influencing the future career of girls in the domain. This position paper explores
the strategic importance of empowering the next generation of female scientists
through the provision of quality science education. It posits that inclusive and
gender-responsive science education at all levels is critical for nurturing interest,
competence, and confidence among young girls and continuing the production of
female research scientists for the future. Drawing on global best practices,
empirical evidence, and national policy frameworks, the paper emphasizes the
need for deliberate mentorship programs, improved access to new and quality
learning resources, and institutional reforms in pulling down system-fostered and
culturally-supported obstacles to female involvement in science. This paper is of
the view that quality science education for women and girls will foster and
strengthen the pipeline of female scientists and sustain support with the ripple
effect of harnessing the full spectrum of human capital for innovation, socioeconomic
advancement, and inclusive nation-building.

Environmental and Health Implications of Improper Disposal of Used Batteries: A Case Study Using Fluted Pumpkin (Telfairia occidentalis) as a Bioindicator

2025-10-01T23:08:28+00:00

The improper disposal of used batteries constitutes a significant environmental
and public health hazard, particularly in developing nations where waste
management infrastructures are inadequate. This study investigates the extent of
soil, water, and plant contamination resulting from battery dust exposure,
employing Fluted Pumpkin (Telfairia occidentalis) as a bioindicator species. A
controlled greenhouse experiment was conducted, wherein fifteen pots were
subjected to graduated concentrations of battery dust (0g, 10g, 20g, 30g, and 40g),
and corresponding water and soil samples were analysed using Atomic Absorption
Spectroscopy (AAS) for the presence of cadmium (Cd), chromium (Cr), copper
(Cu), lead (Pb), and nickel (Ni). Findings revealed a significant, positive correlation
between battery dust concentration and heavy metal accumulation in both
environmental matrices and plant tissues (p < 0.0001). Lead and cadmium levels
exceeded internationally recommended thresholds, posing substantial
carcinogenic risks. Water samples contaminated with 40g battery dust exhibited
Pb concentrations over 700 mg/L, dwarfing the WHO guideline of 0.01 mg/L. Soil
samples similarly demonstrated alarming contamination, with Cu and Pb
concentrations surpassing 1000 mg/kg and 2400 mg/kg, respectively.
Bioaccumulation in Telfairia occidentalis seeds remained within safety limits;
however, the presence of heavy metals indicates latent ecological risks. This study
underscores the urgent need for stringent regulatory enforcement, robust public
sensitisation campaigns, and sustainable recycling systems to mitigate the longterm
environmental and health impacts of battery waste. Our findings contribute
critical empirical evidence supporting policy formulation for safe battery
management and environmental preservation.

GPS-Enabled Vendor to Rider Assignment for Urban last Mile Logistics Optimization

2025-10-01T23:08:30+00:00

The rapid growth of urbanization and e-commerce has heightened the demand for
efficient last-mile delivery solutions in urban logistics. This project addresses the
inefficiencies in traditional systems by developing a GPS-enabled vendor-to-rider
assignment system tailored for urban last-mile logistics optimization. Leveraging
real-time GPS data, the proposed system dynamically assigns delivery tasks based
on proximity, traffic conditions, and order priorities, significantly improving
delivery efficiency and customer satisfaction. Key features include real-time
tracking, interactive mapping, dynamic route optimization, and integrated
communication tools for seamless vendor-rider interactions. Using an agile
development methodology, the system architecture incorporates scalable micro
services, cross-platform mobile applications, and data-driven algorithms for
optimal performance. Preliminary evaluations reveal an 18% reduction in delivery
times and a 99% reliability rate for notifications, validating the system's capability
to enhance logistics operations. This research contributes to advancing urban
logistics by addressing critical challenges in delivery coordination, paving the way
for future enhancements through predictive analytics and AI-driven optimization.

An Integrated Approach to Process Optimization and Quality Monitoring Manufacturing Industry

2025-10-01T23:08:31+00:00

This study applied multiple linear regression analysis in conjunction with
statistical process control (SPC) to monitor and improve the quality of plastic
bottle production. Process inputs such as additive level, melt temperature,
injection speed, mold temperature, cooling time, and ambient temperature were
analyzed against three key quality outputs: tensile strength, surface quality score,
and dimensional precision. X̄ control charts were used to detect variations in each
quality characteristic, while regression models identified which process inputs
significantly influenced these outcomes. Results revealed that additive level and
melt temperature were most impactful on tensile strength, mold temperature and
cooling time influenced surface quality, and injection speed and mold temperature
strongly affected dimensional precision. Sensitivity analysis on the surface quality
model showed that optimized input values could align output performance with
control chart expectations, confirming the utility of regression for process
optimization. The study concludes that integrating regression analysis with SPC
provides a statistically grounded approach for identifying critical variables and
improving product quality in manufacturing environments.

Assessment of the Radiological Impacts of Radionuclides in Soil Samples of Flood Ravaged Areas in Isoko North and South LGA, Delta State, Nigeria

2025-10-01T23:08:32+00:00

This dissertation investigates the impact of natural radioactivity in soil samples
collected from flood-affected areas in the Isoko North and South Local
Government Areas (LGAs) of Delta State, Nigeria. The aim was to identify
potential radiological health risks in these areas, where periodic flooding may
affect the distribution of naturally occurring radioactive elements.
Gamma spectrometry detectors were used to examine soil samples from both
flood-damaged and non-flooded control points in order to quantify the activity
concentrations of radionuclides 238U, 232Th, and 40K. The analysis found that
the average activity concentrations of 238U ranged from 25.67 Bqkg⁻¹ in floodprone
areas to 8.13 ± 1.73 Bqkg⁻¹ in control zones. The concentrations of 232Th
and 40K also varied, with notable ranges in flood-exposed areas compared to
control samples, highlighting an increase potentially linked to flood dynamics.
Radiological hazard indices, such as the Absorbed Dose Rate (ADR), Annual
Effective Dose Equivalent (AEDE), and Excess Lifetime Cancer Risk (ELCR),
were calculated. ADR values ranged from 14.58 nGyh⁻¹ to 26.98 nGyh⁻¹, while
AEDE values averaged 25.20 μSvy⁻¹, both well below UNSCEAR's recommended
safety thresholds. The study reveals that flooding alters soil radionuclide
distribution, with most areas remaining within safe radiation limits. However,
elevated 238U levels in some flood-prone sites exceed WHO thresholds,
highlighting the need for continued monitoring and assessment of potential risks.
This research concludes by suggesting that Isoko North and Isoko South LGAs
conduct periodic evaluations to track potential cumulative effects on public health
and the environment, thereby providing crucial data to guide future
environmental safety and public health efforts in flood-affected areas.

Empırıcal Analysıs of Flood Impact on Food Securıty in Nıgerıa’s Coastal Regıons

2025-10-01T23:08:33+00:00

One common and recurrent natural disaster associated with the coastal region of
Nigeria is flooding. The disaster created by flooding is exacerbated by climate
change and rising sea levels with huge consequences on food availability,
accessibility, stability and utilization. The impact of flood is profound and
multifaceted in the region. Therefore, this study seeks to empirically explore and
examine the impact of flooding on food security in the coastal region of Nigeria.
Mixed statistical methods of Multiple Regression Analysis (MRA) and
Geographically Weight Regression (GWR) were utilized to assess the relationship
between flooding and food security indicators. Data for the study was collected
from the coastal communities in Nigeria affected by flooding over the years. The
findings demonstrated a significant negative impact of flooding on food security
in the coastal region of Nigeria. GWR analysis reveals that the severity of food
insecurity is more pronounced in communities with lower social economic status
and limited adaptive capacities. The study established that the selected
independent variables are statistically significant, an indication of the negative
impact of flooding on crop yields. Thus, the study underscores the critical need
for targeted intervention to enhance food security in flood affected coastal
regions of Nigeria.

Investigating Fibromyalgia Detection via SMOTEENN-fused XGBoosted Stacked Learner

2025-10-01T23:08:34+00:00

The Fibromyalgia syndrome is a chronic pain disorder that affects between 2-4%
of global population with diagnostic challenge that relies on subjective symptom
assessment, and absence of specific biomarkers. This leads to delayed diagnosis
and suboptimal result for patients. However, learning schemes have been explored
to aid quick detection of fibromyalgia with limitations including small data set,
single-model dependence, and mishandle of complex clinical data relations. These
have necessitated the deployment of a more robust analytical frameworks, which
this study seeks to advance that will address the gaps. We propose an ensemble
that fuses three (3) base learners with XGBoost. With dataset imbalance resolved
via SMOTEENN, results show superior performance with our ensemble achieving
1.000 for Accuracy, F1 and Precision with 0.999 for Recall and 18secs runtime
efficiency. Report affirms our ensemble's enhanced generalization, and validates
its fusion with proper feature selection and data balancing can substantially
improve fibromyalgia detection to provide clinicians with a robust tool for early
diagnosis to facilitate timely intervention strategies and improved patient care
outcomes in clinical settings.

Pilot Study for Optimization Strategy for A Smart Monitoring and Alert Energy Consumption Ensemble: A Case of Enhanced Security

2025-10-01T23:08:36+00:00

There is today, a global shift caused by digital revolution for energy use efficiency,
optimization, and consumption via its consequent adoption of sensor units for
optimal-fit, and effective management solutions that will in turn – ripple across the
society as its outcome, improved performance with reduced consumption as a new
norm. Sensor-based units are eco-friendly with environ, consumption, health and
regulation issues that replace traditional solutions with improved quality. We
advance a sensor-design to observe societal condition associated with energy
consumption in home appliance. It utilizes machine learning to analyze total
energy consumed by each appliance and delivers optimal consumption that
reduces energy waste. System as tested across multiple features to handle
expansive input without delay or data losses – yielded the desired effectiveness,
reliability, and efficiency; And affirm its performance stability even with more
device connected.

Comparing the Performance Rating Between Neutral Network and Autoregressive Integrated Moving Average Model for Vehicle Traffic Prediction Estimation

2025-10-01T23:08:37+00:00

Traffic congestion poses significant challenges worldwide, resulting in lost hours
of travel time and increased fuel consumption. Accurate traffic prediction is
crucial for mitigating these issues. Traditional traffic prediction approaches such
as ARIMA are limited by their inability to handle large datasets, inaccurate
predictions, and time constraints. This study explores the use of Feed Forward
Back Propagation Artificial Neural Network (FFBPANN) in the determination of
traffic prediction. Five different FFBPANN architectures were created to
determine the optimal topology. The results show that the architecture with 20
hidden layer neurons achieved the best performance, with a mean square error of
0.19188 at 5 epochs. The implementation of FFBPANN can enhance traffic
management systems, reduce congestion, and improve urban mobility, ultimately
contributing to more efficient transportation networks.

Markov Model Analytics of a Wireless Local Area Network Distributed Coordination Function

2025-10-01T23:08:38+00:00

The present day heterogeneous wireless system is a hybrid of cellular, Wireless
Local Area Network (WLAN) and Worldwide Interoperability Microwave
Exchange (WiMAX) networks. The influence of WLAN assisted in the spread of
the hybrid network to almost all facets of life cannot be over emphasized. The
WLAN which is considered to be the hub of the hybrid has issues with scaling when
the distribution coordination function (DCF) protocol in WLAN hotspot is used.
This suggests that the DCF hotspot has problem in handling multimedia packets.
Therefore, the DCF becomes the burden of this study. This work presents a robust
study of the DCF protocol by applying the simple one-dimensional Markov chain.
It formulates useful numerical expressions for computing the quality of service
(QoS) of the WLAN utilising three scenarios. Scenario one investigated the WLAN
that has infinite buffer storage capacity. A special case of a WLAN without buffer
was studied in the second scenario, while scenario three presented a case with a
limited buffer capacity. Equations representing nodes transmitting with equal
probabilities, the expected number of counter slots between transitions as well as
the probability that a backlogged station transmits in a random slot were
presented. The probabilistically stochastic model was utilized in determining the
collision, idle and transition probabilities, and the throughput of the WLAN DCF.

Development of a Credıt Card Fraud Detectıon System Usıng Artıfıcıal Neutral Network and Support Vector Machıne

2025-10-01T23:08:39+00:00

Credit card fraud has without hesitation an expression of criminal intent and
deception. Fraud identification seems to be a complicated problem that requires a
significant amount of skill until the emergence of machine learning algorithms
were deployed for their classification and detection. However, it is an
implementation for both the better of machine learning as well as artificial
intelligence, ensuring that perhaps the funds of both the customer seems to be
secure and therefore not manipulated. This project therefore proposed the
development of an improved credit card fraud detection system using machine
learning algorithms. The proposed model deployed a fusion of support vector
machine and artificial neural network algorithms for the classification and
detection of credit card fraud was developed using feature driven development
methodology which deploys a feature centric approach to program development
combining different requirement components to meet user needs. The system was
trained and tested with credit card fraud datasets from Kaggle machine learning
repository split into 70:30 ratio for training and testing purposes respectively.
After 20 epochs the model performance outperformed the existing system with an
accuracy of 99.83%, precision 100%, recall 100% and F1-score 100% respectively.

Development of a Cellular Radio Frequency Modulation Transmitter-Receiver System

2025-10-01T23:08:40+00:00

Frequency Modulated (FM) signal transmitter is a device for transmitting
Frequency Modulated signal over short range. The project is aimed at designing
independent transmitter-receiver system suitable for FUPRE academic
environment with extension to industrial, scientific and medical spheres. This
document outlines a straight forward and cost-effective design technique for
constructing a FM transmitter using simple electronic components like resistor,
capacitor, inductor etc. The transmitter was tuned to 101.0 MHZ at open space as
well as building, and trees while the FM receiver as well cellular FM radio receivers
were located at a radius of about 300m from the transmitting point. The system
was also tested at the lecture theater one auditorium close to the library, and
Tetfund one building (old Tetfund) at Federal University of Petroleum Resources,
Effurun, Delta State. A clear voice signal was received at these locations.

Dementia Detection and Management Using Wearable Device fused Deep Learning Scheme

2025-10-01T23:08:41+00:00

Dementia affects over 50 million people worldwide, with numbers expected to
triple by 2050. Traditional diagnostic methods often lack early detection
capabilities and real-time monitoring, leading to delayed interventions and
increased caregiver burden. This study aimed to develop an integrated dementia
detection system combining wearable Internet of Things (IoT) with deep learning
for early identification and continuous monitor of dementia. The system has three
core components: (1) a wearable IoT using ESP32 and MAX30102 sensors to
collect data, (2) a deep learning scheme to compare five neural network approaches
(MLP, LSTM, GRU, CNN, and hybrid CNN-LSTM), and (3) a mobile app to ease
data visualization. The dataset comprised of 1,510 records with 11 features.
Preprocessing handled missing values, categorical encoding, and feature scaling;
while, SMOTE was used to address class imbalance. Results showed that the MLP
demonstrated superior performance with a 97% accuracy, 100% sensitivity, 94%
specificity, and 0.98 AUC-ROC. Device successfully collected physiological data as
displayed over mobile app – enabling real-time monitor and prediction. Device
yields a significant advancement in dementia care with early detection, continuous
monitoring, and improved accessibility. MLP offered exceptional performance
with the practical wearable implementation, provides a scalable solution for
healthcare systems seeking to improve dementia diagnosis and management while
reducing caregiver burden.

Security Enhancement Using Multifactor Authentication Strategy for the Solenoid Door Access Control and Management: A Pilot Study

2025-10-01T23:08:42+00:00

Traditional door access control systems predominantly rely on single or dualfactor
authentication mechanisms, making it vulnerable to credential theft,
unauthorized access and spoofing attacks. We implement a multifactor
authentication approach to enhance security using a fused knowledge-based (PIN),
possession-based (RFID), and inherence-based (biometric) authentication for
secure door access control. The system use an ESP32 microcontroller as central
processing unit, interfaced with RFID readers, biometric sensors, and a mobile
application for comprehensive user verification. Performance evaluation
conducted over 30 days demonstrated 98.7% authentication accuracy, average
response time of 3.2 seconds, 100% spoofing resistance, and 99.97% system
uptime. Comparative analysis with conventional two-factor systems revealed
significant improvements in security resilience, with 45% better resistance to PIN
brute force attacks, 75% improvement in RFID cloning resistance, and 80%
enhancement in access log integrity. The proposed system addresses critical
vulnerabilities in traditional access control mechanisms while maintaining userfriendly
operation, making it suitable for deployment in high-security
environments such as data centres, educational institutions, and government
facilities.

Assessing the Impact of Land Use Change on Urban Heat Island Intensity in Lagos, Nigeria

2025-10-01T23:08:44+00:00

Urban Heat Island (UHI) effects, driven by rapid urbanization and land use
transformations, pose serious environmental and public health risks in rapidly
developing cities. Lagos, a mega-city characterized by intense population growth
and sprawling infrastructure, offers a compelling context for investigating the
dynamics between land use change and surface temperature variations. This study
evaluates the extent to which increased built-up areas contribute to elevated
surface temperatures, using a combination of satellite-derived land use
classifications and remote sensing thermal imagery. Landsat imagery from 2000,
2010, and 2022 were processed using Geographic Information System (GIS) tools
to extract land cover types, while Land Surface Temperature (LST) was derived
using the thermal infrared bands. A regression-based spatial model was developed
to analyze the correlation between built-up expansion and UHI intensity over the
years. Findings reveal a significant increase in built-up land cover from 23.6% in
2000 to 47.2% in 2022, accompanied by a rise in average LST by 4.5°C. The spatial
distribution of UHI hotspots closely aligns with zones of intense urban
development, particularly in areas such as Ikeja, Apapa, and Lagos Mainland.
This empirical evidence underscores the critical need for integrating climatesensitive
urban planning approaches, including green infrastructure and zoning
reforms, into Lagos’ development agenda. The study bridges existing research gaps
on UHI in West Africa by offering a longitudinal, data-driven analysis. It provides
actionable insights for policymakers aiming to enhance urban resilience amidst the
climate crisis.

A Real-Time Artificial Intelligence System for Collaborative Document Editing and Workload Optimisation

2025-10-01T23:08:45+00:00

Managing and collaborating on documents has long been integral to work,
education, and communication. In the digital age, these tasks demand greater
speed, flexibility, and security. This dissertation proposes a Real-Time AI-Powered
Document Editing System for Collaborative Workflows, designed to address the
challenges of simultaneous multi-user editing, access control, and data protection.
The primary aim is to develop a platform that enables multiple users to edit the
same document in real-time with synchronized updates and intelligent conflict
resolution. The system integrates an AI-driven permission management module
that assigns and adjusts user roles dynamically, based on contextual analysis and
behavioral patterns. To safeguard document integrity and user data, the system
also includes encrypted file access, automated activity logging, and access anomaly
detection. The solution is developed using an Object-Oriented Approach, paired
with the Rapid Application Development (RAD) methodology. This ensures a
modular, user-focused design process with frequent iterations and fast
prototyping. The choice of RAD supports responsive adaptation to user feedback
and evolving requirements during the development cycle. This research
demonstrates how the combination of real-time collaboration, artificial
intelligence, and secure design principles can enhance document management
systems. The outcome is a scalable and intelligent platform that aligns with the
growing demand for collaborative, efficient, and secure digital workspaces.

An Enhanced Customer Management and Schedulıng System

2025-10-01T23:08:46+00:00

This study introduces an enhanced customer management and scheduling system
aimed at improving task assignment for handling customer queries, follow-ups,
and standby duties. Traditional scheduling methods often struggle in dynamic
environments where query volumes, priorities, and resource availability change
frequently. These challenges lead to poor customer satisfaction, inefficient resource
use, and delayed responses. The problem’s combinatorial nature, classified as NPhard,
makes it computationally difficult to find optimal schedules, especially as
task complexity increases. To address these issues, the Agile Software Development
Methodology was adopted to design and implement a flexible and efficient
scheduling system. The system adapts to changing conditions and optimizes
personnel allocation for customer service tasks, ensuring better responsiveness and
resource utilization. The results of the implementation showed remarkable
improvements across key performance areas. Portability increased by 35%, ease
of use and user experience improved by 45%, and accessibility was enhanced by
50%. Operational costs were reduced by 60%, while customer access time
improved by 70%. Appointment scheduling time was reduced by 65%, and data
security was strengthened by 50%. These improvements demonstrate the system’s
effectiveness in delivering a more reliable, user-friendly, and cost-efficient solution
for managing customer interactions and scheduling tasks in dynamic environment.

Phytochemical Screening, GC Analysis and Antimicrobial Activity of N-Haxane Extract of Cissus arguta Hook.f. (Sunset bell)

2025-10-02T10:10:26+00:00

This study focused on determining the phytochemicals and the antimicrobial
efficacy of the non-polar extract of the fresh stem of Cissus arguta using n-hexane.
The phytochemical classes were determined by standard methods followed by Gas
Chromatography – Mass Spectroscopic (GC-MS) analysis. Disc diffusion (Agar)
method was used to determine the microbial sensitivity of extract against selected
strains of micro-organisms, while broth dilution was used to determine the
Minimum inhibitory concentration (MIC). The percentage yield of the extract was
1.03 %, revealing alkaloids, glycosides, saponins, steroids, tannins, and terpenoids
via phytochemical studies. GC-MS results indicated free fatty acids as the major
constituents (54.24 %) of the C. arguta n-hexane extract, closely followed by esters
and terpenoids (41.50 %). The hydrocarbons were of alicyclic derivatives, with the
least percentage composition (4.25 %). The extract showed antimicrobial potency
against Escherichia coli (MIC:12.5 mg/mL; MBC: 25 mg/mL), Pseudomonas
aeruginosa (MIC: 25; MBC: 25), Staphylococcus aureus (MIC: 25 mg/mL; MBC:
50 mg/mL), Bacillus subtlis (MIC:50 mg/mL; MBC: 100 mg/mL), Asperigillus
niger (MIC: 25 mg/mL; MFC: 50 mg/mL). and Candida albicans (MIC:50 mg/mL;
MFC: 100 mg/mL). The extract of C. arguta shows antimicrobial activity against
all class of microbes, with the highest zones of inhibition against P. aeruginosa and
S. aureus, but least active against the fungi strains. Specifically, C albicans show
the highest resistance against the extract. The microbial activity and
phytochemicals justify its folkloric usage.Antimicrobial

The Impact of Cognitive Psychology and Memory in ICT Utilization Among Female Academia in STEM Fields

2025-10-06T12:53:58+00:00

This study addresses how cognitive psychology and memory processes influence
the adoption and utilization of Information and Communication Technologies
(ICT) among female academia in Science, Technology, Engineering, and
Mathematics (STEM) fields. As ICT continues to reshape academic environments,
understanding the psychological underpinnings of its use becomes essential,
particularly in underrepresented groups such as women in STEM. Using a mixedmethods
approach, data were collected from 120 female STEM academia across
five Nigerian universities through structured questionnaires and in-depth
interviews. Results from the study show that higher cognitive load was significantly
negatively correlated with ICT usage frequency (r = -0.43, p < 0.01), and working
memory scores were positively associated with self-reported ICT competence (β =
0.38, p < 0.05). Findings reveal that cognitive factors such as working memory,
self-efficacy, and cognitive load significantly impact ICT usage patterns. Moreover,
age and academic rank moderate the relationship between memory retention and
ICT proficiency. The paper recommends that cognitive-based training programs
and institutional policies that support ICT competence and development, should
be implemented in Higher Education Institutions for female scholars in STEM.

Quest for Ground-Truth or Stochastic Myth by Leveraging the AI-Powered Wearable Device for Dementia Disease Detection: A Pilot Study

2025-10-07T04:45:47+00:00

Dementia affects over 50 million people worldwide, with numbers expected to
triple by 2050. Traditional diagnostic methods often lack early detection
capabilities and real-time monitoring, leading to delayed interventions and
increased caregiver burden. This study aimed to develop an integrated dementia
detection system combining wearable Internet of Things (IoT) with deep learning
for early identification and continuous monitor of dementia. The system has three
core components: (1) a wearable IoT using ESP32 and MAX30102 sensors to
collect data, (2) a tree-based, stacked learning approaches with 3-base classifiers
(decision tree, random forest and adaboost) – and a XGBoost meta-classifier, and
(3) a mobile app to ease data visualization. The dataset comprised of 2,149 records
with 16-features. Preprocessing handled missing values to ensure data
quality/integrity – while, normalization was used to address imbalanced dataset.
Results showed that the stacked model yielded a 99.7% accuracy, 100% sensitivity,
99.4% specificity, and 99.8% AUC. While IoMT device successfully collected
physiological data as displayed over mobile app – model shows that the AIPowered
unit can effectively help detect dementia.

Impact Of Oil-Related Activities on the Abundance, and Diversity of Herbaceous Plants in Delta State, Nigeria

2025-10-07T10:23:45+00:00

This research was necessary to establish a baseline understanding of how oilrelated
activities in Delta State, Nigeria, impact the abundance, diversity, and
conservation status of herbaceous plants, which is crucial for developing effective
environmental management strategies. This study provided a detailed analysis of
plant diversity and distribution patterns across five sampling locations: MW1,
MW2, OE1, OE2, and CT. The inventory revealed the area's rich botanical
diversity, documenting numerous plant families and species, many of which have
reported medicinal uses, highlighting their value to local communities. However,
the conservation status of several species remains uncertain, with many classified
as "Data Deficient" or "Not Evaluated" by the IUCN, underscoring the urgent
need for further conservation assessments. The quantitative analysis showed clear
variations in plant abundance and community structure across the sites. The
Poaceae family was the most abundant, with specific species like Eleucine indica,
Euphorbia aphylla, and Ipomoea eriocarpa also showing high individual
abundances. Diversity indices revealed that site MW1 generally had the highest
diversity and evenness, suggesting a more balanced and complex plant community
compared to other locations. Principal Coordinate Analysis (PCoA) and cluster
analysis were used to explore relationships between plant species and sampling
locations, providing insight into community composition and patterns of similarity
and dissimilarity among sites. The study's findings provide a crucial ecological
baseline for the area, informing future conservation strategies, resource
management, and ongoing ecological research, particularly regarding the factors
influencing plant distribution and diversity in this specific ecosystem.