FUPRE Journal of Scientific and Industrial Research (FJSIR)

Modeling Meiotic Rearrangements: Using Dynamic Programming to Elucidate the Role of DNA Alignments in Cilliate Reproduction

2025-04-16T01:13:35+00:00

Dynamic programming algorithms are powerful tools for analyzing complex
biological data, including DNA sequences. In this study, we employed a
combination of global and local alignment algorithms, affine gap and star
alignment algorithms, and multiple alignment algorithms to analyze DNA
sequences obtained from different ciliates during meiotic reproduction. Our
analysis revealed that these algorithms were effective in identifying conserved
regions and patterns in the DNA sequences, and in constructing phylogenetic trees
that reflected the evolutionary relationships among the sequences. Specifically, we
found that the global alignment algorithm was useful for identifying long stretches
of identical nucleotides, while the local alignment algorithm was effective in
detecting shorter, conserved regions. The affine gap model allowed us to account
for the presence of gaps in the sequences, while the star alignment algorithm
enabled us to identify conserved regions that were shared among multiple, closely
related sequences. Finally, the multiple alignment algorithm allowed us to
compare the DNA sequences of multiple ciliates simultaneously, and to identify
conserved regions that were shared among all of the species studied. Our findings
have important implications for our understanding of the evolution and diversity
of ciliates and other organisms, and highlight the utility of dynamic programming
algorithms in analyzing complex biological data. Overall, our study provides a
framework for using dynamic programming algorithms to analyze DNA
sequences, and demonstrates the potential of these algorithms to provide insights
into the genetic factors that underlie evolution and diversity in a wide range of
organisms.

Assessment of Sanitation practices and the level of VOCs in cowhide in some Abattoirs in Delta state, Nigeria

2025-04-16T01:13:36+00:00

This study investigates the sanitation practices, and levels of volatile organic
compounds (VOCs) in roasted cowhide in some abattoirs in relation to established
health standards. A mixed-methods approach was employed, involving a
demographic analysis of the workforce, surveys on sanitation practices, alongside
laboratory testing for VOCs in roasted cowhide. A comprehensive demographic
analysis revealed a predominance of older, married males, with 78% at Agbarho
and 73% at Effurun Market being aged 46 years and above, and a high level of
educational attainment, as 86% of respondents at Agbarho and 90% at Effurun
had completed secondary school. Both abattoirs demonstrated strong adherence
to cleaning and disinfection practices, with 100% compliance for daily cleaning
and the use of VOC-free cleaning agents, although Effurun exhibited significant
deficiencies in inventory management for cleaning supplies, achieving only 22%
compliance in the schedule for replacing cleaning supplies. Notably, both locations
displayed inadequate compliance in ventilation system maintenance and air
quality control measures, with only 10% adherence to ventilation inspection and
waste disposal evaluation reported at both abattoirs. The analysis of VOC levels
in roasted cowhide from Effurun were alarmingly high, with benzene detected at
0.0237 mg/kg (more than twice the WHO limit of 0.01 mg/kg), while Agbarho
showed no detectable levels of benzene. Additionally, compounds like 1,2-dibromo-
3-chloropropane were found in concentrations of 0.1145 mg/kg in Effurun and
0.5509 mg/kg in Agbarho, both exceeding EPA limits. Overall, the findings
emphasize the urgent need for enhanced regulatory compliance and improved
sanitation practices in both abattoirs to safeguard public health.

A Survey of Smart City Development and the Role of Internet of Things

2025-04-16T01:13:38+00:00

The Internet of Things (IoT) is an innovation that has improved the old way of
system existence into an evolutionary high-tech system. Examples of such systems
that are transformed by this technology are: smart urban areas, smart households,
pollution control, energy-saving, smart transport systems, smart manufacturing
owing to IoT. Various critical research training and surveys are done to improve
the technological system using IoT. There are some issues considered at different
facets of IoT applications, such as supporting technologies, socially and
ecologically. This article discussed the applications of the Internet of Things (IoT)
in smart city development, the diverse problems and vital issues of IoT design, and
key applications in some cities in Nigeria. Some existing literature and their role in
diverse areas of IoT were discussed. The article offers deeper understanding on the
need for smart cities and IoT’s role in smart city development and smart mobility.

Geochemical Assessment of Soil in Dumpsites Around Tombia-Amassoma Road, Western Niger Delta

2025-04-16T01:13:39+00:00

This research work focuses on determining the urban geochemistry of soil in
dumpsites around Tombia-Amassoma road, western Niger Delta. Nine (9) soil
samples were randomly collected within the dumpsite vicinity and two (2) control
samples were collected from a distance of about 9.0 km from the dumpsite. The
samples were prepared according to standard procedures and analyzed for some
heavy metals (Ag, Zn, Cu, Ni and Fe) using the Atomic Absorption
Spectrophotometer at Analytical Concept Environmental Consultants Laboratory
Ltd, Port Harcourt, Nigeria. The geochemical data was subjected to multivariate
statistical analysis and comparisons were made with World Health Organization
standard. The results obtained showed that the mean concentrations of Ag
(0.54mg/kg), Zn (33.89mg/kg), Cu (31.85mg/kg) and Ni (22.80mg/kg) were within
an acceptable limit when compared with WHO standard, while Fe (6329.40mg/kg)
exceeded the limits. Geo-accumulation index and contamination factor revealed
that the soils around the dumpsite were moderately to heavily contaminated with
Fe thereby indicating that the sediment samples are polluted. The study concluded
that the soils around the Tombia-Amassoma dumpsite was contaminated with Fe
and as such should be discouraged in its usage for agricultural related purposes as
these highly toxic trace elements can be absorbed by plants. Therefore, a wellengineered
landfill that incorporates the local geology and the topography of the
area should be designed so as to prevent infiltration of heavy metals into the soil
and shallow groundwater system.

Geospatial Assessment of Medical Emergency Response Competencies in the Federal University of Petroleum Resources Effurun, Delta State

2025-04-16T01:13:40+00:00

This research on Medical Emergency Response and Management, conducted to
addresses the increasing prevalence of medical emergencies and the inadequacies
in response measures. Using the Federal University of Petroleum Resources as case
study, the research assessed medical emergency response resources against the
Offshore Petroleum Industry Training Organization Medical Emergency
Response Procedure criteria as standards to evaluate their sufficiency. Findings
revealed that the competency level of medical emergency response personnel at
FUPRE fell short of the criteria standard, particularly due to the absence of Tier
0 and Tier 1 competencies, as well as critical First Aid equipment such as
Automatic External Defibrillator. While 71.6% of respondents reported
recognizing or witnessing a medical emergency, only 9.5% were aware of the
emergency call number pertaining to medical emergencies. Statistical analysis
showed P-values of 1.96E-5 and 3.26654E-77 for Tiers 0 and 1 competency,
respectively, indicating a significant need for formal competency development, as
these values were below the 0.05 threshold. Consequently, the null hypotheses were
rejected, affirming the necessity for formal competency development training for
Tier 0 and Tier 1 MER personnel. The study concludes with recommendations for
management and government-driven policies to establish humanitarian
organizations, such as the Nigerian Red Cross, within public institutions to close
competency gaps. It also advocates for continuous education to raise awareness
and increase funding to address these deficiencies, in line with Sustainable
Development Goal 3, which focuses on promoting health and well-being for all.

An Approach to Design Variables for Analysis of Conditionally Automated Driving Transitions

2025-04-16T01:13:41+00:00

In conditionally automated vehicles, drivers can occasionally activate the autopilot
to perform driving tasks such as steering and braking. During this time, the driver
may participate in secondary tasks such as reading and monitoring the
surrounding. However, the driver must stay alert to a potential request to resume
driving in situations that cannot be managed by the autopilot. The situation
awareness of drivers in such driving situations that cannot be managed by the
autopilot is required to avoid accidents during the transition. Previous studies
indicate that various variables such as complexity of surrounding traffic
conditions, secondary tasks, speed of subject vehicle, and previous takeover
transition experience affect takeover performance. However, the approach to
utilize these variables to enable efficient takeover transitions have not been
determined. This contribution discusses a systematic design of these variables. It
utilizes known dimensions of complex dynamical systems and fundamentals of
human cognitive processes to design driving scenarios and secondary tasks. The
characteristics of the variables are systemically varied to generate different driving
situations to better understand how they determine takeover transitions and
interaction.

Optimal Placement of TCSC and DPFC on Nigeria Power Transmission Network for Capacity Enhancement

2025-04-16T01:13:42+00:00

The increasing demand for electrical energy and the high costs and challenges
associated with building new transmission lines highlight the need to optimize
existing power transmission networks. This study focuses on optimizing or
enhancing Nigeria's 28-bus power transmission system using Flexible Alternating
Current Transmission System (FACTS) devices, specifically, Dynamic Power Flow
Controller (DPFC) and Thyristor-Controlled Series Compensator (TCSC). This
research employed the Bacterial Foraging Optimization Algorithm (BFOA) in
MATLAB to determine the optimal placement and sizing of DPFC and TCSC to
minimize power losses, voltage deviations, and installation costs. Simulation results
were compared, before and after incorporating the FACTS devices. In the base
case, 32% of the buses had voltage profiles outside the acceptable range (0.95 to
1.05 p.u.), with real power loss of 602.91 kW and reactive power loss of 4559.69
kVAR. After optimally placing two DPFC and two TCSC devices in the system, all
bus voltages were regulated to fall between 0.95 and 1.06 p.u., while real and
reactive power losses were reduced by approximately 84.6% and 84.7%,
respectively. This demonstrates a significant improvement in the efficiency and
capacity of the power transmission network.

Enhanced Voltage Stability and Loss Minimization through Optimal Placement and Sizing of Dstatcom in Distribution Networks

2025-04-16T01:13:43+00:00

Integrating Distributed Static Compensators (DSTATCOM) into distribution
networks improves voltage profiles, reduces power losses, and enhances stability.
This paper optimizes DSTATCOM placement and sizing within the 34-bus
Thinkers Corner 6.0 MVA, 33/11KV distribution network in Enugu, Nigeria,
using Particle Swarm Optimization (PSO) in MATLAB R2017b. The Backward-
Forward Sweep method was employed to simulate load flow analysis, both with
and without DSTATCOM, to assess real and reactive power losses, voltage
profiles, and voltage stability indices. Validation of the model was performed using
the IEEE 33-bus system to ensure the robustness of the approach. Results show
that DSTATCOM integration reduces losses, stabilizes voltage, and improves
network efficiency.

The Removal of Cr3+ And Pb2+ from Aqueous Solution using Modified Starches from Forest Anchomanes (Anchomanes difformis)

2025-04-16T01:13:44+00:00

Water contamination by heavy metals is a global challenge requiring eco-friendly
remediation. Progressive discharge of heavy metals in large water bodies has
necessitated the need to look for cheap and readily available adsorbents for
removal of dissolved metals ions in waters. The potential of modified forest
Anchomanes starches as adsorbent for the removal of Cr3+ and Pb2+ are presented
in this study. Starch from forest Anchomanes tubers was isolated, chemically
modified by carboxylation and acetylation processes and characterized with FTIR.
On the adsorption of the metal ions, the effects of pH, contact time, starting
concentration, and adsorbent dosage were examined. Batch adsorption kinetic and
isotherm studies were also done for the uptake. The optimum condition for
adsorption were 150 mins, pH 6 and 1.2 g/L adsorbent dosage. The adsorption
isotherm studies indicated that the Langmuir model was suitable and appropriate
for the adsorption of Cr3+ onto acetylated (ACS) adsorbent while Freundlich
isotherm model was suitable for the uptake of Cr3+ onto carboxymethylated (CMS)
adsorbent and Pb2+ uptake on both adsorbents. Pseudo-second order kinetics best
describe adsorption of Cr3+ and Pb2+ to ACS whereas, Elovich and Pseudo-firstorder
kinetic models best describe adsorption of Cr3+ and Pb2+ on CMS
respectively. The results indicate that modified Anchomanes difformis starches are
effective and efficient in the removal of Cr3+ and Pb2 in wastewater.

A SMART FAULT DETECTION SYSTEM USING FUZZY LOGIC TECHNOLOGY

2025-04-16T01:13:45+00:00

In the present era, automobiles have become an integral aspect of individuals' daily
lives. Engine malfunctions can prime to noteworthy issues for regulars if not
distinguished early, punctually addressed, and truthfully repaired. Such letdowns
may pretense risks to existence and property, negatively impacting customer
pleasure and the status of vehicle firms. This novel approach leverages the flexible
properties of fuzzy logic, which are strategically applied to strengthen and improve
the operational effectiveness, safety, and dependability of automotive systems. To
ensure that the prototypical is skillful of handling a wide range of complex and
varied automotive data, ML.NET was used to train the dataset that was obtained
from the Kaggle Dataset repository. Impressively, this forward-thinking system
was developed utilizing an extensive array of state-of-the-art web machineries,
including Bootstrap 3.5, JavaScript, ASP.Net, CSS and JQuery, and SQL server,
attesting to its commitment to technological advancement and innovation.
Rigorous testing and meticulous evaluation have yielded promising outcomes,
showcasing the system's potential for widespread adoption while demonstrating
its prowess in averting accidents, curbing maintenance costs, and significantly
enhancing the overall driving experience. Achieving a commendable performance
accuracy of 73.14%, alongside a precision rate of 100% and an F1 Score peaking
at 76.62%, this visionary system stands at the forefront of transformative progress
in automotive fault detection, promising a paradigm shift in vehicular safety
protocols and maintenance standards

Sequestration of Methylene Blue from aqueous solution by cellulose of Sand box (Hura crepitans L.) seed shells

2025-04-16T01:13:47+00:00

The capacity of cellulose from sand box (Hura crepitans) (HC) seed shells for the
removal of Methylene blue dye from aqueous solutions was examined. FTIR and
XRD analysis showed functional groups and crystalline nature of the cellulose.
Utilising Scanning Electron Microscopy (SEM), the porosity of the adsorbent was
examined. Using 0.5 g mass of the adsorbent at pH 12.0, initial dye concentration
of 20 mgL-1, contact period of 120 min and temperature of 25 °C, the adsorption
parameters showed the greatest dye removal effectiveness of 98.41%. The kinetic
models of Pseudo-First order, Pseudo-Second order and Intra-particle diffusion
were used. Whereas the Langmuir, Freundlich, Temkin, and Dubinin-
Radushkevich isotherms were used to fit the equilibrium data. The kinetic data
best suited the Pseudo-Second order model, whereas the Langmuir isotherm most
accurately reflected the equilibrium data. Thermodynamic parameters observed
are ΔHo (1.550 kJmol-1), ΔSo (1.846 Jmol-1K-1) and ΔG° (1.013 kJmol-1),
respectively. Methylene blue adsorption is endothermic and spontaneous,
according to these values. 93.05% of the adsorbent was recovered according to the
desorption study. Thus, cellulose derived from Hura crepitans (HC) is a very
effective choice for adsorbents used in the removal of pollutants and dyes in
solution.

Design, Development and Simulation Analysis of Aluminium Gasket Cutting Machine for Industrial Application

2025-04-16T01:13:48+00:00

This research investigated the design and development of aluminium Gasket
cutting machine for industrial application. A gasket is a mechanical seal that closes
or fill the spaces between two members basically to prevent leakages must
especially when under compression. A gasket cutter is a machine used in the
fabrication of gaskets as desired. Gasket therefore are relevant in the oil and gas
sector as relevant in the oil and gas sector as leakages of any sort will cause
monumental losses. This multiple use imposes a high demand for the product
hence the need to domesticate the technology. The method adopted in the design
and developed includes the sourcing of local raw materials relevant to the design.
All mathematical and scientific principles relevant it is design was developed. The
maximum strain of 0.16 was obtained at 0.36 second at maximum Von Mises stress
of 2.25 x 106 N/m2. A load of 10N was applied and released in the period of (end
time) of 5 seconds. It is more important to note that this study enables one to truly
understand and optimize design product performance and utilize the concept of
lean engineering. It is hoped that both government and non-government agencies
will key with project of mass producing and commercializing this machine, this
return would increase the country Gross Domestic Product (G.D.P).

Effects of Antiretroviral Therapy (ART) on the Transmission Dynamics of HIV

2025-04-16T01:13:49+00:00

Antiretroviral Therapy (ART) has revolutionized the management of
HIV/AIDS, significantly reducing morbidity and mortality rates among
infected individuals. This study explores the effects of Antiretroviral
Therapy (ART) on the transmission dynamics of HIV, employing a
mathematical modeling approach to analyze the interactions between
various population compartments. The study demonstrates that ART
significantly lowers viral loads to undetectable levels, thereby reducing the
risk of HIV transmission. Through sensitivity analysis, we identify key
parameters influencing treatment outcomes, particularly highlighting the
critical role of ART adherence and initiation rates in shaping community
transmission dynamics. The findings emphasize the necessity of addressing
barriers to ART access and adherence, especially among vulnerable
populations. Furthermore, this work contributes to public health strategies
aimed at achieving the UNAIDS 90-90-90 targets by providing insights into
the effectiveness of ART as a cornerstone of HIV prevention and treatment
programs. Overall, our analysis underscores the importance of continued
research and innovation in HIV management to enhance treatment efficacy
and improve health outcomes for individuals living with HIV.

A Hand Gesture Controlled Vacuum Cleaning Robot

2025-04-16T01:13:50+00:00

This contribution describes the design and construction of a semi-autonomous
robot vacuum cleaner which can be controlled by hand gestures. The robot
vacuum cleaner is controlled with the use of a gesture control system consisting of
an Arduino Nano microcontroller for gesture recognition as well as control value
assignment and an Arduino UNO for coordination and navigation. The
microcontrollers are interfaced with a MPU6050 Accelerometer for gesture
detection and RF module for communication. The hand gesture control system is
designed to demonstrate how gesture-control can be utilized as a means of
navigation with respect to domestic robots as well as the simplification of humanmachine
interaction in domestic contexts.

A SURVEY OF SMART CITY DEVELOPMENT AND THE ROLE OF INTERNET OF THINGS

2025-04-16T01:13:51+00:00

The Internet of Things (IoT) is an innovation that has improved the old way of
system existence into an evolutionary high-tech system. Examples of such systems
that are transformed by this technology are: smart urban areas, smart households,
pollution control, energy-saving, smart transport systems, smart manufacturing
owing to IoT. Various critical research training and surveys are done to improve
the technological system using IoT. There are some issues considered at different
facets of IoT applications, such as supporting technologies, socially and
ecologically. This article discussed the applications of the Internet of Things (IoT)
in smart city development, the diverse problems and vital issues of IoT design, and
key applications in some cities in Nigeria. Some existing literature and their role
in diverse areas of IoT were discussed. The article offers deeper understanding on
the need for smart cities and IoT’s role in smart city development and smart
mobility.

Vehicular Movement Prediction Via Supervised Vector Machine

2025-04-16T01:13:52+00:00

In major urbanized cities, the increase in the number of vehicles on the road at
certain periods creates traffic gridlocks. Most road users at these peak periods
experience traffic congestion during these peak periods. This has resulted in the
loss of hours of work, delays in travel time, accidents, and even loss of life Most
special vehicles like ambulances and emergency vehicles are trapped in the grid
lock. Hence, there is a need to have a better predictive model for vehicle movement.
In this paper, a vehicle prediction system which was developed using Supervised
Vector Machine in a python environment using road variables, such as road
condition, type of vehicle, weather condition and time of day vehicle. The dataset
was obtained from Kaggle online dataset. It was evaluated using the following
evaluation metrics: Root Mean Square Error (RMSE 2.845), Sum of Square Error
(SSE 809.6798875823043), R-Square (R2 -0.14387416306270362), and Adjusted RSquare
(R2 -0.21767249616352324 ).

Development of a Zigbee-Based Tyre Pressure Monitoring System

2025-04-16T01:13:53+00:00

Tyre pressure monitoring systems (TPMS) are crucial in vehicle safety and
performance. Direct TPMS use sensors within the tyre assembly for real-time
monitoring of the tyre pressure and transmits it to report on an onboard display.
Most TPMS technology uses 315 or 434 MHz Ultra High Frequency (UHF) Signal.
In Nigeria, this frequency is used in the television spectrum which could result in
interference. This paper, used an innovative low-power, low-cost ZigBee
alternative to develop the TPMS. It covers the design, implementation and testing
of the TPMS. The system comprises ZigBee sensors placed in the tyre assembly to
measure tyre pressure and temperature and transmit the data to a ZigBee
coordinator in the car. In turn, the ZigBee coordinator sends the aggregated data
to a graphical Liquid crystal display, (GLCD) to show the real-time data of each
tyre to the driver. A GSM module helps report the tyre’s state to the user if the
vehicle is parked and the tyre pressure is below 26 psi. The hardware circuit was
designed in Proteus 8.7 and the printed circuit board (PCB) for the transmitter
and receiver was developed and fabricated. MPXHZ6400AC6T1 piezoresistive
pressure sensor and DS18B20 temperature sensor were used to sense the tyre
pressure and temperature respectively. The software was developed using the C
programming language. The developed system was installed in a 2000 model-year
Toyota Camry for operational testing. A standard gauge was used to measure the
tyre pressure and compare it with the developed system to determine the accuracy
of the TPMS data. The results show the accuracy of the TPMS data. Future work
will explore sensor accuracy optimizations and integration with advanced vehicle
diagnostics systems and a mobile application.

RHS-doped FAU Zeolite-Y Catalyst Synthesis for Transesterification of Jatropha oil

2025-04-16T01:13:54+00:00

This study investigated the synthesis of faujasite zeolite catalysts from
aluminosilicate-containing natural sediments modified by biowaste silica in the
catalysis of transesterification of jatropha oil. Zeolite was synthesized by the
hydrothermal method with NaOH at 3.07g/mol, SiO2 at 10.50g/mol, and Al2O3 at
2.61g/mol were the optimum amount required in the synthesis of a zeolite-Y in an
autoclave. Transesterification reaction was conducted on constant temperature
magnetic stirrer fitted with a reflux condenser. Functional group study of
synthesized zeolite revealed that silicone (Si – O – Si) was highly prevalent at
1095.8cm-1 in IR spectrum. XRD and EDS examination showed a faujasite zeolite-
Y with Si at 85.60g and Al at 17.50g with Si/Al ratio of 4.891g/mol. The synthesized
zeolite catalysis in transesterification reaction yielded 91.63% biodiesel which had
0.17% FFA. Biodiesel characteristics met ASTM standard having kinematic
viscosity of 4.97mm2/s and flashpoint of 113°C. The fatty acid profile showed that
fatty acid constituent was mostly linoleic acid methyl ester (30.29%) and
hexadecanoic acid (20.81%), from GCMS analysis. The synthesized faujasite
zeolite-Y, with its large mesoporous structure, exhibits remarkable catalytic
activity in the transesterification of jatropha oil to produce biodiesel.

Performance Evaluatıon of Beamformıng in 5G OFDM Systems and Advantages over 4G

2025-04-16T01:13:55+00:00

This paper investigates the performance of 5G networks. It focuses on the
implementation and advantages of Orthogonal Frequency Division Multiplexing
(OFDM) and beamforming compared to 4G LTE systems. Through MATLAB
simulations, it examines critical performance metrics, including signal-to-noise
ratio (SNR), throughput, and latency, highlighting the benefits of 5G in enhancing
data transmission rates, reducing latency, and supporting higher user density.
Beamforming, a core component in 5G, enables directional signal transmission,
significantly improving signal quality and reducing interference, especially in
dense and urban environments. The results indicate that 5G, with its advanced
technologies, outperforms 4G by delivering improved connectivity, increased
network capacity, and low-latency communication, which are essential for
emerging applications like IoT, autonomous vehicles, and augmented reality.
Limitations related to frequency bands, simulation constraints, and scalability are
acknowledged, with recommendations for future research focused on diverse
testing conditions, higher frequency bands, and optimization techniques. This
study underscores the transformative potential of 5G technology in modern
wireless communications, setting the stage for future advancements as we transit
toward 6G.

Correlation between Climate Parameters and the Spread of Malaria in a Southern City of Nigeria

2025-04-16T01:13:56+00:00

Malaria is a familiar sickness in the tropics. The occurrence of malaria outbreaks
is governed by several factors like rainfall and temperature. This study assessed
the association between climatic parameters (temperature and rainfall) and
malaria occurrence in Benin City, Nigeria. Climate data was obtained from the
Nigeria Meteorological Agency (NIMET) covering the period from 1991 to 2022.
Malaria incidence data was collected from the University of Benin Teaching
Hospital covering 2014 to 2022. The data obtained was analysed using Mann
Kendall test and multiple linear regression analysis in SPSS Version 25. The
results showed that monthly temperatures were generally highest in February and
least in August, and the annual temperature trends revealed a rise that is
statistically significant (P=0.000) over the period studied. Rainfall reveals no
statistically significant trends (P=0.41). The study also revealed that there was a
correlation between climate parameters (temperature and rainfall) and malaria
prevalence (R=0.598). Also, rainfall and temperature contributed to only 35.8% of
malaria occurrence over the study period. This can be inferred that climate
parameters to some extent influence malaria prevalence in the study area.
However, factors other than climate parameters contribute immensely to the
prevalence of malaria in the study area. Therefore, public awareness should be
carried out on climate change and malaria risk so as to control malaria prevalence.

Systematic Strategies for Decarbonizing Nigerian Energy and Transport Sectors

2025-04-16T01:13:57+00:00

The Nigerian energy and transport sectors heavily relied on fossil fuels, and electricity to
thrive in modern economy. Thus, it has elevated the levels of energy poverty and emissions,
and the untapped potential of renewable energy sources. The alarming consequences of
climate change is another factor that have fueled a global movement to transition of cleaner
energy sources. Remarkably, Nigeria has actively embraced this movement, demonstrating
its commitment through unwavering participation in the Paris Agreement. This study seeks
to develop energy plan model that serves as an input/output simulation tool designed for
energy system analysis. The proposed model is specifically tailored towards a high
renewable energy system. Also, a strategic energy planning, and economic analyses that
emphasizes the integration of renewable energy to achieve low carbon energy penetration
was developed. The model was validated by comparing the energy plan simulations with
actual data from Nigeria's energy system in 2020. Results show the environmental and
economic benefits of increasing renewable energy percentages in the energy mix. The
techno-economic analysis focuses on Wind, River Hydro, and Solar technologies for 2050.
Simulations reveal the linear relationship between renewable energy supply and reduced
CO2 emissions and total annual costs. The study suggests scaling up Hydroelectricity,
followed by Solar and Wind energy, based on economic considerations, water availability,
and technology reliability.

A framework for Machine Learning- based Fall from Height Prediction in Construction Industry

2025-04-16T01:13:58+00:00

The construction industry is undeniably one of the most hazardous sectors, where
workers face a multitude of risks daily. Among these risks, falls from height (FFH)
stand out as a significant concern, accounting for a substantial proportion of fatal
and nonfatal injuries. Over the years, with the advent of advanced technologies
and data analytics, there has been a growing interest in leveraging Machine
Learning (ML) and artificial intelligence (AI) techniques to enhance fall risk
assessment and prevention. This paper provides a comprehensive, concept-centric
literature review of FFH, exploring its evolution, diverse models, the use of
machine learning and artificial intelligence techniques for better assessment and
prevention as well as extensive applications. This paper presents a framework for
an explainable machine learning-based model for proactive FFH prediction of in
construction sites. The framework leverages the predictive power of random forest
classifier, a robust ensemble learning method, along with the interpretability
offered by the Local Interpretable Model-agnostic Explanations (LIME)
framework. It also critically addresses key challenges such as lack of transparency
in the use of machine learning models in FFH predictions and its consequent effect
of limiting trust among users. By evaluating the evolution and current state of FFH
research, this paper reviewed the significant trends, uncovers existing gaps, and
suggests potential direction for future work. This research work, therefore aims to
deepen the understanding of this crucial domain in the construction industry that
is receiving traction and disturbing publicity.

The Adaptive Nonparametric Regression Model and Its Residuals with a Mixing Parameter for Response Surface Methodology: A Novel Blend

2025-04-16T01:14:00+00:00

The modeling stage of response surface methodology (RSM) includes the
application of regression models to estimate the functional relationship between
the response and the explanatory variables which demands using data generated
from an appropriate experimental design. In RSM, the Ordinary Least Squares
(OLS) is traditionally used to model the data via user-specified low-order
polynomials. The OLS model tend to underferformed when the homoscedasticity
assumption is sullied. In the literature, the use of semiparametric regression
models is the preferred techniques in RSM, becauce it combines features of
parametric and nonparametric regression models, unlike the nonparametric
regression models that are affected by the idiocyncracies of RSM data. In this
paper, we consider a novel integration (blend) between an existing adaptive
nonparametric regression model and a locally adaptive bandwidths selector
generated from the explanatory variables for adequate smoothing of the data. The
adaptive nonparametric regression model incorporate local linear regression
(LLR) portion and product of the optimal mixing parameter and, the residuals of
the LLR to provide a second opportunity of fitting part of the data that were not
captured by the LLR model and while the locally adaptive bandwidths addresses
the problems associated with dimensionality, sparsity of RSM data and cost
efficient design. In the application of RSM data, two data type were considered,
and we observed that the goodness-of-fits statistics, zero residual plots, and
optimization results of the novel integration (blend) model when compared with
the OLS, Model Robust Regression 1(MRR1) and Model Robust Regression 2
(MRR2) considerably performed better.

A Computer Adaptive Test Framework Leveraging Genetic Algorithm for Optimizing Testlet-Based Question Selection and Randomization

2025-04-16T01:14:01+00:00

Computer Adaptive Testing (CAT) has ushered in a new era in assessment by
providing a more efficient, accurate, and tailored testing experience for measuring
each test-takers' abilities. However, optimizing question selection and
randomization remains a significant challenge. In the framework of CAT lies the
question selection and randomization mechanism, which determines the next item
to be administered based on the test-taker's responses. Traditional approaches to
question selection often rely on Heuristic Randomization (HR), which can lead to
suboptimal item selection and compromised test validity. To address these
limitations, this study proposes a novel CAT framework that leverages Genetic
Algorithms (GA) to optimize testlet-based question selection and randomization.
The methodology adopted supports Object-Oriented Analysis and Design (OOAD)
using Agile Methodology. The proposed Genetic Algorithm Randomization (GAR)
employs a multi-parameter fitness function, incorporating question difficulty,
discrimination, time, and learning objective coverage. Through iterative evolution,
the algorithm identifies the optimal question set combination that maximizes test
efficiency. By harnessing evolutionary principles, GAR optimizes the selection of
CAT test questions. The results demonstrate that the possibility of optimizing CAT
with GAR for a better question selection and randomization process. The findings
of this research have significant implications for the development of more
sophisticated CAT systems, ultimately leading to improved assessment outcomes
and better-informed decision-making in education and professional certification.

Enhanced Health (Record) Information Management System Using Mobile Application Development Framework

2025-04-16T01:14:02+00:00

n essential component of effective healthcare delivery is health record
management. Although, conventional paper-based systems have long been the
standard, they have a number of drawbacks, including inaccuracy, inefficiency,
and a lack of accessibility. The adoption of a web-based health record management
system proved to be a viable replacement for the paper-based system for handling
medical records. However, there were certain obstacles along the way, including
concerns about data security and privacy, data loss during the shift from paper to
the web-based system, a lack of inclusivity, unauthorised access to patient records,
and limited access to patient records because of inadequate internet access and
facilities in Nigeria. This dissertation therefore reviewed the observable
limitations of the existing system and proposed the development of a mobile
patient’s health record management system using mobile application development
framework. The system was developed using Android Studio integrated
development environment (IDE) and the Java programming language, with
SQLite database and rapid application development methodology (RAD) which is
an Agile method for enhanced program development especially for mobile
application development. The new system outperformed the existing web-based
hospital record management system in terms of speed, efficiency, robustness
portability, data security and integrity as it enables both patients and doctors to
access and book appointments remotely without having to go physically to the
hospital saving man-hour and boosting the morale of both staff and patients.

An Optimized Machine Learning Model for Population Growth Prediction Using Artificial Neural Network and Genetic (Neuro-Genetic) Algorithm

2025-04-16T01:14:03+00:00

With the global population growth reaching over seven billion persons on the
planet, issues of desertification, famine, global warming and climate change are
presently in the front burner of global discourse as the need for proper and more
accurate population prediction for economic and national development become
expedient. This paper therefore proposes’ the optimization of machine learning
model for population prediction in Nigeria using artificial neural network and
genetic algorithm. The model was adopted to dataset for the national population
commission (NPC) containing annual population growth from 1950 – 2021. The
Agile Software Development Methodology (SDM) was applied to real-life data to
evaluate the efficiency of the model. An iterative approach was adopted to time
series data in other to examine the applicability of the proposed model. The model
was implemented in Java Apache NetBeans using 70% of the dataset for training
and 30% as test data. The model yielded an accuracy of 76% with a Root mean
squared errors (RMSE) of 8.21%, mean Absolute percentage errors (MAPE) of
6.4%. Mean squared errors (MSE) of 5.67%, MEA of 23.29% and MAD of 49.23%
when compared to existing models in literature.

Sedimentary Facies and Grain Size Distribution; Insights into Ogwashi-Asaba Formation, Niger Delta Basin

2025-04-16T01:14:04+00:00

The Niger Delta is renowned for being one of the most productive hydrocarbon
provinces globally. This productivity is primarily attributed to its complex
geological history, which is marked by intricate stratigraphy and various
sedimentary facies. These sedimentary facies, which are distinct bodies of sediment
with specific characteristics, provide essential information about past
environments and sedimentary processes, about the complex stratigraphy and
diverse sedimentary facies. Stratigraphic sequences in the Niger Delta include
alternating layers of sandstones, shales, and siltstones. Sediment samples were
collected with cores too, from the Ogwashi-Asaba formation, and a detailed field
analysis was documented. Grain size, petrographic, and geochemical analysis were
carried out in the lab, from the samples collected. The analyzed parameters suggest
that the sandstone samples from the Ogwashi-Asaba Formation are
predominantly fluviatile. Graphic Mean (Mz) ranges from 1.4 to 2.1 phi. Grain
Size Composition 2-20% very fine sand, 6-66% fine sand, 45-87% medium to
coarse sand, and 70-100% very coarse sand. Sorting (σ1) ranges from 1.1 to 2.1
phi, with 64.71% of sediments being very poorly sorted and 35.29% poorly sorted.
Graphic Skewness (Sk) ranges from -0.6 to +0.5, indicating varying energy
conditions. Graphic Kurtosis (KG): Ranges from 0.8 to 1.9, with curves varying
from leptokurtic to platykurtic. These sedimentary processes shaping the
Ogwashi-Asaba formation creates a diverse stratigraphy and a variety of
sedimentary facies. Each provides valuable insights into past environmental
conditions and sedimentary dynamics. A comprehensive understanding of these
processes enhances the potential for hydrocarbon exploration and the reservoir
characterization of the province

Effect of Buoyancy, Velocity Power Index and Magnetohydrodynamics (MHD) Heat and Mass Transfer of Second-Grade Nanofluid flow over a Stretching-Porous Sheet with Chemical Reaction

2025-04-16T01:14:05+00:00

The second-grade plays a significant effect on the velocity of fluid knowing that
it possesses a characteristic of non-Newtonian fluid having the velocity field
with two derivatives in stress strain tensor relationship. In this study, the
combined effects of buoyancy forces, velocity index and
Magnetohydrodynamics (MHD) heat and mass transfer of second-grade
nanofluid flow over a stretching-porous sheet with chemical reaction is
considered. The nanofluid being examined in this study is the Copper (Cu) with
water (H2O) as the base fluid. The partial differential equations governing of
the flow are non-dimensionalized, transformed using the stream function and
the similarity variables. Hence, solved numerically using the mid-point
Richardson extrapolation code in MAPLE 2021 for various values of the
controlling parameters of the flow. The results are presented in graphs and
tables. From the study, it is observed that the increase in the buoyancy due to
temperature parameter (

Response Surface Methodology for Optimizing and Simulating the Extraction of Anti- Diabetic Compounds from Cucumis Sativus

2025-04-16T01:14:06+00:00

The growing prevalence of diabetes mellitus has intensified the search for effective,
plant-based therapeutic agents with minimal side effects. Cucumis Sativus
(commonly known as cucumber) has been recognized for its potential anti-diabetic
properties, attributed to its rich phytochemical profile. This study aims to optimize
and simulate the extraction process of anti-diabetic compounds from Cucumis
Sativus using Response Surface Methodology (RSM), a statistical and
mathematical tool effective for modeling and analyzing problems where multiple
variables influence the desired outcome. In the literature, experimental data were
fitted to a second-order polynomial regression model, and the model's adequacy
was confirmed through with a high R² value indicating a strong predictive
reliability. A Central Composite Design (CCD) was employed to systematically
evaluate the influence of four independent variables; extraction temperature (°C),
extraction incubation-time (minutes), agitation speed (rpm), and volume of solvent
(mL) on the yield of bioactive compounds exhibiting