Journal of Basic Physical Research by Faculty of Physical Sciences
AN IMPROVED MULTINOMIAL NAÏVE BAYES ALGORITHM WITH LAPLACE SMOOTHING FOR NEWS CLASSIFICATION: THE BISALLAH NEWS NETWORK MODEL (BNNM)
Naïve Bayes remains one of the most widely used algorithms for text classification and sentiment analysis, yet its application to Nigerian newspaper social-media data has not been previously documented. This study develops and evaluates an improved Multinomial Naïve Bayes classifier - incorporating TF-IDF feature weighting and Laplace (additive) smoothing - for the automatic classification of Nigerian newspaper tweets into politics, sports, and business categories. Drawing on a cleaned corpus of 24,679 tweets from The Punch, Vanguard, and The Guardian (2015–2018), the classifier was trained on a 70/30 train-test split and tuned via grid-search cross-validation across n-gram ranges, TF-IDF normalisation schemes, and smoothing parameters (α). The resulting model - termed the Bisallah News Network Model (BNNM) - achieved an overall classification accuracy of 83.1%, with category-level prediction accuracies of 80.6% for politics, 72.1% for sports, and 96.0% for business, an average recall of 0.75, and an average F1-score of 0.77. These results are broadly comparable to, though in some cases below, prior studies (e.g., Preety & Dahiya, 2015, at 89.01%; Perdana & Pinandito, 2018, at 83.8%), with differences attributable to corpus size and composition. The study demonstrates that an improved, Laplace-smoothed Multinomial Naïve Bayes pipeline can deliver practically useful classification performance on noisy, real-world Nigerian social-media text, offering a foundation for automated content categorisation and editorial decision-support tools in African digital newsrooms.
A DEEP LEARNING ENABLED FRAMEWORK FOR AUTOMATED CASSAVA CROP DISEASE DETECTION
Cassava is a major staple crop in sub-Saharan Africa and plays a crucial role in food security and rural livelihoods, but its productivity is significantly affected by plant diseases that are often difficult to detect at early stages using traditional visual inspection methods. These conventional approaches are typically slow, subjective, and unreliable, especially in resource-limited farming environments. This study presents the development of a deep learning-based cassava crop disease detection system aimed at improving early diagnosis and supporting effective cassava crop management. The system is built using a Convolutional Neural Network (CNN) enhanced with transfer learning to enable efficient extraction of discriminative features from cassava leaf images. A hybrid development methodology combining Agile, DevOps, and Rapid Application Development (RAD) principles was adopted to support iterative development, continuous testing, and user-centered improvements involving agronomists and farmers. The model was trained using a combined dataset of field-collected images and publicly available plant disease repositories covering five classes: Cassava Mosaic Disease (CMD), which stands for Cassava Mosaic Disease, Cassava Brown Streak Disease (CBSD), which stands for Cassava Brown Streak Disease, Cassava Green Mite (CGM), Cassava Bacterial Blight (CBB), and healthy cassava leaves. To enhance robustness under real-world conditions, preprocessing techniques such as resizing and normalization were applied, alongside data augmentation methods including rotation, flipping, and brightness adjustments. The system was evaluated using accuracy, precision, recall, F1-score, and confusion matrix analysis, achieving an overall accuracy of 75%. Strong performance was observed in detecting CMD and CBSD, while lower performance occurred in distinguishing healthy leaves and early-stage infections due to visual similarity between classes. Overall, the results demonstrate that CNN-based approaches are effective for cassava disease detection and can support farmers in early diagnosis, reduce crop losses, and improve agricultural productivity.
HYDROCHEMICAL ASSESSMENT OF GROUNDWATER IN IKENYI AREA SOUTHEAST NIGERIA
The most resilient and sustainable source of water supply in Ikenyi area is groundwater. Unfortunately, groundwater in the area is plagued by contaminants of geogenic origin which is suspected to be the cause of the frequent occurrence of water borne disease in the area. Hydrochemical analysis of water samples from the area was carried out to identify the key contaminants and their spatial distribution in the area. The work revealed that wells in the area which encountered aquifer between the depths of 16m to 22m are mostly contaminated with heavy metals. The heavy metals with concentration values higher than WHO recommended maximum allowable concentration are Iron, Lead, Zinc, Arsenic and Chromium. While anomalous concentration values for Arsenic and Chromium are recorded in less than 40% of the tested water samples, Iron, Zinc and Lead anomalous concentration values occur in 80% to100% of the samples. Heavy metal anomalies are predominantly in the Northeast of the study area. Water quality index based on the weighted arithmetic index method indicates that the tested groundwater is good to poor in the Southeast, mostly very poor in the west and unfit for consumption in the Northeast. The similarity of the Water quality index map derived from weighted arithmetic index and Heavy metal pollution index demonstrated that the water quality in the study area is driven by the heavy metal anomaly. Given the prevalent heavy metal contamination in the study area, it is imperative to consider a robust water treatment plan in any groundwater development project in the area.
GEO-ACCUMULATION INDEX OF SOME HEAVY METALS IN THE WASTE DUMPSITE AT AGU AWKA ANAMBRA STATE, NIGERIA
Uncontrolled disposal of municipal solid waste continues to pose serious environmental challenges in many developing countries, particularly through the release of toxic heavy metals into soil systems. This study evaluates the concentration, distribution, and contamination status of selected heavy metals (Pb, Cd, Ni, Cr, and As) in soils from the Agu-Awka dumpsite, Nigeria, using the Geo-accumulation Index (Igeo). Soil samples were collected at three depths (0–5, 15, and 30 cm) from two active dumpsites and a control site. Physicochemical properties were analyzed using standard procedures, while metal concentrations were determined via atomic absorption spectrophotometry following aqua regia digestion. The results show clear enrichment of heavy metals in dumpsite soils compared to the control, with the highest concentrations observed in surface layers. Igeo classification indicates moderate to moderately strong contamination, particularly for lead and arsenic. Although concentrations were generally below international guideline limits, the index-based approach reveals notable anthropogenic influence. The vertical distribution pattern suggests gradual leaching, raising concerns about potential groundwater contamination. Overall, the findings highlight the need for improved waste management strategies and consistent environmental monitoring to reduce long-term risks.
AN ENSEMBLE LEARNING FRAMEWORK FOR SIGN LANGUAGE TRANSLATION USING BIDIRECTIONAL CNNS AND TRANSFORMERS
Effective communication is essential for human interaction, yet individuals with hearing impairments and speaking difficulties often face significant challenges. The ability to recognize and translate sign language in real-time can bridge the communication gap between those who do not know sign language and those who rely on it. This work examines various sign language conventions prevalent in Nigeria and beyond, including distinct phonetic and semantic structures, as well as the messages they convey. It covers languages such as American Sign Language (ASL), Bura Sign Language (BSL), Yoruba Sign Language (YSL), Hausa Sign Language, and Adamorobe Sign Language (Ghana). This work utilized an object-oriented programming (OOP) methodology. The work was built using Python for backend development, with machine learning libraries such as TensorFlow, Pandas, and NumPy. The user interface was developed using React and Node.js. The system implemented an ensemble learning-based bidirectional sign language translation protocol using Convolutional Neural Networks (CNN) and the Bidirectional Encoder Representations from Transformers (BERT). These architectures were combined in a forward and backward encoder mechanism to translate general sign languages, ensuring both high performance and robustness. Community data gathering, validation, refinement, and analysis techniques were employed to create a reliable and diverse dataset. The model achieved great performance with an accuracy of 98.7%, a precision of 97.6%, and an F1 Score of 98.2%. It was tested on ASL datasets (both images and videos) and community feedback from language experts.
SPATIO-TEMPORAL ANALYSIS OF CARBON MONOXIDE CONCENTRATIONS AT MAJOR ROAD JUNCTIONS IN OWERRI, NIGERIA: EVALUATING THE IMPACT OF TRAFFIC CONGESTION AND ENGINE IDLING
Carbon monoxide (CO) poisoning is a seamless and dangerous problem in developing countries like Nigeria. Chronic exposure to low or moderate concentrations of CO cause harm to the body and can lead to death when the concentration is high. This study conducted an analysis of CO levels at three strategic high-traffic intersections within the Owerri Municipal, two high concentrated junctions; Control Junction, and Ama J.K., and one mild-concentrated junction (Rock View Junction) using a Crowcon Gasman electrochemical sensor. Data were collected simultaneously across three diurnal time blocks over a five-day period to capture peak commute fluctuations. The results reveal that CO concentrations significantly peak during morning rush (08:00–11:00) and evening peak (16:00–19:00) rush hours, driven primarily by high traffic density, prolonged engine idling, and the prevalence of poorly maintained second hand vehicles. Control Junction recorded the highest mean concentration of 54.67 ppm, with peak values reaching 58.1 ppm. Ama J.K. junction recorded a mean value of 46.33 ppm, with a peak value of 56.5 ppm, while Rockview recorded a mean value of 38.27 ppm with a peak value of 47.9 ppm The concentrations obtained significantly exceed the updated WHO (2021) safety thresholds for urban environments.
A BIG DATA-DRIVEN ANALYTICAL FRAMEWORK FOR PREDICTING CUSTOMER PRODUCT PREFERENCES AND CHURN IN NIGERIA
The rapid growth of Big Data has transformed organizational decision-making and operational efficiency across industries, particularly in the telecommunications sector, where understanding customer behaviour is crucial for reducing churn and optimizing revenue. This study presents a Big Data-driven analytical framework for predicting customer product preferences and churn in MTN Nigeria, leveraging a dataset of 974 anonymized customers with 158,943 transactional records collected between 2024 and early 2025. The methodology integrates data preprocessing, normalization, class imbalance correction using SMOTE, and supervised machine learning algorithms, including Random Forest (RF), Backpropagation Neural Network (BPNN), K-Nearest Neighbours (KNN), and Naïve Bayes (NB). Models were trained and evaluated using accuracy, precision, recall, and F1-score metrics, with RF achieving the highest overall performance (accuracy = 89%, F1-score = 0.94). The framework was implemented as a Python-based prototype system with modules for customer segmentation, product targeting, churn risk prediction, and personalized marketing communication. Results demonstrate that the proposed system can accurately predict customer preferences, reduce unsolicited promotions, and improve customer retention and revenue. This study highlights the potential of integrating Big Data analytics and machine learning into telecom CRM workflows and provides a scalable model applicable to other sectors requiring behaviour-driven customer engagement strategies.
IMPROVING THE FORECASTING ACCURACY OF DYNAMIC CONDITIONAL CORRELATION (DCC) GARCH MODELS FOR EXCHANGE RATES OF EMERGING MARKET CURRENCIES
This study examines the performance of Dynamic Conditional Correlation (DCC)-GARCH and hybrid DCC-GJR-GARCH models in modelling and forecasting exchange rate volatility in emerging markets, with a focus on Nigeria and South Africa. Daily exchange rate data for USD, GBP, and EUR against the Nigerian Naira (NGN) and South African Rand (ZAR) from January 2020 to May 2025 were analysed. The models were estimated under both multivariate normal and multivariate Student-t distributions to account for the stylised facts of financial returns, including volatility clustering, heavy tails, and potential asymmetry. Empirical results reveal that exchange rate returns exhibit significant leptokurtosis and time-varying volatility. The Student-t distribution consistently outperforms the normal distribution across all models, as evidenced by higher log-likelihood values and lower AIC and BIC criteria, indicating the importance of capturing fat tails in exchange rate modelling. While the DCC-GJR-GARCH model incorporates asymmetric effects, the leverage parameter was largely insignificant across most currency pairs, suggesting weak asymmetry in exchange rate volatility. Forecast evaluation using RMSE, MAE, and the Diebold-Mariano (DM) test shows that the DCC-GJR-GARCH model provides superior predictive accuracy compared to the standard DCC-GARCH model for most exchange rate series. The findings highlight the relevance of combining dynamic correlations with heavy-tailed distributions in improving volatility forecasts, offering valuable insights for risk management and policy formulation in emerging financial markets.
SATELLITE-BASED ASSESSMENT OF TOTAL COLUMN NITROGEN HOTSPOTS IN NIGERIA: IMPLICATIONS FOR URBAN IN-SITU MONITORING NETWORKS
Nitrogen Dioxide (NO2), a criteria pollutant, serves as a primary indicator of anthropogenic combustion and a precursor to respiratory health crisis In Nigeria. The scarcity of terrestrial air quality infrastructure in the country, creates a significant "data blind spot "for environmental regulators. This study utilizes NASA Giovanni (Geo-spatial Interactive Online Visualization and Analysis Infrastructure) to conduct a comprehensive assessment of the Area-Averaged Total Column of nitrogen dioxide NO2 across the Nigerian landmass. The research establishes a 19 year old baseline for atmospheric NO2 loading. The analysis reveals distinct spatial clusters of elevated NO2 over the Lagos-Ogun industrial axis, and the densely populated urban centers of Abuja. Temporal trends extracted via Giovanni time-series tools showed a consistent seasonal peak and troughs during the dry season and the rainy seasons respectively.
A MOBILE-BASED MODEL FOR PROMOTING SUSTAINABLE LIFESTYLE CHOICES THROUGH DIGITAL AND ENVIRONMENTAL LITERACY
Over the years, human activities have significantly increased the concentration of greenhouse gases in the atmosphere, leading to climate change which has become one of the greatest global health threats. Addressing this challenge requires individuals to adopt sustainable lifestyle practices. This paper presents a mobile based model designed to promote sustainable lifestyle choices by integrating digital and environmental literacy. The model features interactive learning modules, feedback mechanism and carbon footprint calculator to raise awareness on environmentally harmful behaviours and guide users towards sustainable alternatives. The calculator estimates carbon emissions from daily activities and provides recommendations on positive behavioural changes in arears such as transportation, waste management, energy consumption and diet. By integrating digital literacy and environmental education, the model empowers individuals to make informed choices that reduce their personal carbon footprints, thereby contributing to climate change mitigation, environmental protection and improved public health.
SIMULATION AND COMPARATIVE ANALYSIS OF AN ENHANCED NOISE CANCELLATION IN ECG SIGNAL DENOISING VIA NORMALIZED LEAST MEANS SQUARE (NLMS) ALGORITHM
The clinical utility of ECG signal can be compromised by the presence of unwanted components and interference in the ECG signals which do not only obscures critical cardiac information but also introduces inaccuracies in the analysis, leading to misdiagnoses and suboptimal patient care. However, noise in ECG signals arises from multiple sources, including muscular activities, baseline wanders, power line interference and electrode artifacts. This work propose a robust enhanced filtering techniques using the Normalized Least Means Square (NLMS) Algorithm designed to successfully cancel noise out of ECG signal while preserving the key diagnostic features. Extensive simulations were conducted using standard PhysioNet ECG Database and the Massachusetts Institute of Technology- Beth Israel Hospital (MIT-BIH) Arrhythmia Database, the MIT-BIH Normal Sinus Rhythm Database, and the MIT-BIH QT Database to validate performance across diverse noise condition. In the most challenging scenario, where the input Signals-to-Noise (SNR) reached as low as -5 dB, the filter achieved a SNR output quantifying approximately 88.96dB. The filter across different noise type, ECG datasets, and signals-to-noise ratio (SNR) levels suggest that the enhanced filtering approach has potential for broader applicability in various clinical contexts and can be integrated into a diverse array of monitoring system.
ANTIOXIDANT AND QUALITATIVE PHYTOCHEMICAL DETERMINATION OF INDIAN SPINACH (Basella alba)
Indian spinach (Basella alba) is a widely consumed leafy vegetable known for its nutritional value and traditional medicinal applications. This study investigated the phytochemical composition and antioxidant activity of the methanolic leaf extract of Basella alba. Qualitative phytochemical screening revealed the presence of flavonoids (++), phenols, alkaloids (++), glycosides (++), terpenoids (++), saponins (++), and triterpenoids (++), while carbohydrates and anthraquinones were absent. Antioxidant activity was evaluated using the DPPH radical scavenging assay, which showed percentage inhibition ranging from 42.41% to 99.12%, with a minimum standard deviation of 0.17. The total phenolic content (TPC) was highest at 50 mg/mL (85.14%) and lowest at 10 mg/mL (37.40%). The ferric reducing antioxidant power (FRAP) assay indicated relatively low reducing capacity, although activity increased with concentration. Although the antioxidant potency of the extract was lower than standard antioxidants, the presence of bioactive phytochemicals supports the ethnomedicinal use of Basella alba as a functional food. These findings provide scientific evidence for its therapeutic potential and highlight the need for further studies to isolate active compounds and optimize extraction methods.
ENGINEERING OPTO-ELECTRONIC PROPERTIES OF CUO-ZNO NANOCOMPOSITE THIN FILMS FOR DUAL-FUNCTIONAL APPLICATIONS IN PHOTOVOLTAICS AND PHOTOCATALYSIS
The opto-electronic properties of metal oxide nanocomposites are highly tunable through compositional control, enabling their application across diverse fields. This study presents a facile chemical bath deposition method to fabricate CuO-ZnO thin films with varying precursor concentrations (0.2M - 0.8M). UV-Vis spectroscopy revealed that the optical bandgap could be engineered from 3.5eV down to 2.2eV, with the 0.4M concentration exhibiting optimal characteristics: the narrowest bandgap, highest absorbance across the visible spectrum, and favourable film thickness. These properties are critically dissected for two key applications. The enhanced visible light absorption and ideal band alignment suggest superior performance as a photoactive layer in low-cost photovoltaic cells. Concurrently, the narrow bandgap and promoted charge separation at the CuO-ZnO p-n junction position this same 0.4M film as an efficient visible-light-driven photocatalyst for environmental remediation. This work demonstrates the successful design of a single, versatile nanomaterial with high potential for advancing both clean energy generation and water purification technologies.
SUBSURFACE STRUCTURAL INTERPRETATION OF PARTS OF SOUTHERN BENUE TROUGH, NIGERIA USING AEROMAGNETIC DATA IMPLICATIONS FOR HYDROCARBON POTENTIAL
This study aimed at carrying out subsurface structural interpretation of parts of southern Benue Trough, Nigeria using aeromagnetic data for implications for hydrocarbon potentials. The study area is bounded by latitude 8o00l N to 9o00l N and longitude 9o00l E to 10o00l E with an area extent of 12,100 km2. The methodology involves the use of four aeromagnetic data sheets namely; 211 (Kwolla), 212 (Shendam), 232 (Akiri) and 234 (Ibi) which were collected from Nigerian Geological Survey Agency and the data were contoured and analysed using Oasis-Montaj, Surfer and GeoRose softwares. The visual assessment of total magnetic intensity and residual anomaly maps of the study area reveal magnetic intensities ranging from -39.0 nT to 154.4 nT. The 3D surface map of the magnetic intensity of the basement reveals the presence of uplifts and depressions, a typical expression of folds. The result of qualitative interpretation of magnetic maps also reveals that the area is intensely faulted with major faults trending in northeast to southwest (NE-SW), whereas, the east to west (E-W) and northwest to southeast (NW-SE) are the minor trend in the area, which align with the major rift system of Benue Trough. The spectral analysis of the potential data revealed sedimentary thickness ranging from 0.47 km to 5.88 km with an average of 2.03 km, which could be investigated for further petroleum exploration. The generated geological model results revealed two geological layers in the area, namely the sedimentary layer and the basement layer. The result shows that the temperature at depth values obtained range between 39.95 oC and 167.56 oC with an average of 76.71 oC and they are shallower at the northern areas than the other areas. The study concludes that the southern and central parts of the study area are good for possible hydrocarbon accumulation in the region.
NON-MONOTONIC EVOLUTION OF OPTICAL AND STRUCTURAL PROPERTIES IN ELECTRODEPOSITED SN- DOPED MGO THIN FILMS: AN OPTIMIZATION AND MECHANISTIC STUDY
The strategic incorporation of tin (Sn) as a dopant into wide-bandgap semiconductors represents an established approach for bandgap engineering. Also, the development of multifunctional optical coatings that offer protection against ultraviolet (UV) degradation while minimizing reflective losses is necessary for enhancing the efficiency and durability of solar cells, display screens, and architectural windows. This study investigates the nonlinear and concentration-dependent effects of Sn doping on the structural and optical properties of magnesium oxide (MgO) thin films synthesized via electro-deposition. Films were fabricated with varying Sn precursor concentrations (2–10 mL) and characterized using X-ray diffraction (XRD) and UV–Vis spectroscopy. XRD confirmed a cubic MgO structure (Fm-3m) with a dominant (200) orientation and crystallite sizes ranging from 21.3 nm to 116.7 nm. Optical analysis revealed a pronounced non-monotonic trend: the 8 mL doped film exhibited maximum absorbance (~0.9) whereas lower (2, 4 mL) and higher (10 mL) concentrations resulted in anomalous or reduced performance, respectively. These films function as a dual-purpose optical coating exhibiting a strong UV-blocking capabilities, with absorption coefficients exceeding 5×10⁸ m⁻¹ in the 200-400 nm range, effectively preventing harmful radiation from penetrating the substrate. The films serve also as an anti-reflective layer in the visible spectrum, achieving low reflectance values of 15-20%. Notably, the 6 mL doped sample maintained a high visible transmittance of ~70% alongside a minimal reflectance of ~15%.These findings provide a crucial roadmap for defect engineering in electrodeposited MgO films.
TRANSFORMING QUALITY PHYSICAL SCIENCE EDUCATION TO MEET THE DEMANDS OF THE FOURTH INDUSTRIAL REVOLUTION
The Fourth Industrial Revolution (4IR) is reshaping the knowledge, skills, and competencies required for global competitiveness, with profound implications for physical science education. Traditional instructional approaches centered on rote memorization and abstract theoretical knowledge is increasingly inadequate for preparing learners to function effectively in a technology-driven society. This study examines strategies for transforming physical science education to align with 4IR demands, with emphasis on critical thinking, problem-solving, creativity, digital literacy, and interdisciplinary learning. A descriptive survey design was adopted, involving 500 participants comprising physical science lecturers and final-year students from tertiary institutions in eastern Nigeria. Data were collected using a structured questionnaire and analyzed using descriptive statistics and thematic analysis. Findings revealed high awareness of 4IR concepts across the large sample but low practical integration of digital tools such as artificial intelligence, simulations, and virtual laboratories.
DETERMINATION OF SEDIMENTARY THICKNESS OF ONITSHA AND ITS ENVIRONS, SOUTHEASTERN NIGERIA USING AEROMAGNETIC DATA
Determination of the sedimentary thickness of Onitsha and its environs, southeastern Nigeria using aeromagnetic data was carried out in order to ascertain the sedimentary piles across the area for possible hydrocarbon accumulation. The study area is situated between latitude 6000lN to 6030lN and longitude 6030lE to 7000lE. The aeromagnetic data sheet 300 was acquired from the Nigerian Geological Survey Agency (NGSA) for the study and has an area extent of 3025 km2. The acquired data was processed and analyzed using Geo-softwares (Oasis Montaj, Surfer- 32 and GeoRose) for both qualitative and quantitative interpretation in order to produce maps and models. The anomalies identified were analyzed using the half-slope method. The result from the qualitative interpretation shows high magnetic intensity ranging from 8000 nT – 8135 nT). Th result also shows occurrence of sparse and linear contours indicating high sedimentary thickness cover. The analytical map and Rose diagram show magnetic lineaments trending in the Northeast-Southwest direction. The sedimentary thickness deduced ranged from 3.06 km – 12.41 km with an average 6.63km. The sedimentary thickness map reveals that the area has a high sedimentary thickness around the Northern-Eastern regions of the study area and also indicates the areas of uplift and depression. The study concludes that the area has a high sedimentary cover, which may possibly favour hydrocarbon generation.
LIPID COMPOSITION AND POTENTIAL HEALTH BENEFITS OF JACKFRUIT (ARTOCARPUS HETEROPHYLLUS) PULP AND SEED
Jackfruit (Artocarpus heterophyllus) is widely cultivated in tropical regions and serves as an important food resource, yet its lipid composition remains relatively underexplored. This study comparatively investigated the fatty acid, phospholipid and phytosterol profiles of Artocarpus heterophyllus pulp and seed oils to evaluate their potential nutritional and health benefits Oil extraction was carried out using Soxhlet extraction with n-hexane, while fatty acids were analyzed as methyl esters using gas chromatography. Phospholipid and phytosterol compositions were determined using standard chromatographic and spectrophotometric methods. The results showed that the seed oil contained a higher total fatty acid content than the pulp oil. Linoleic acid (36.18%) was the predominant fatty acid in the pulp, while myristic (70.70%) dominated the seed oil. The total saturated fatty acids (TSFA) were 25.44% and 79.35%, whereas total unsaturated fatty acids (TUFA) were 74.49% and 21.83% in the pulp and seed, respectively. The pulp exhibited a higher total essential fatty acids (TEFAs) content and a more favourable PUFA/SFA ratio, indicating greater cardiovascular health potential. Phosphatidylcholine was the abundant phospholipid in both samples, with concentrations of 53.80 mg/100 g (pulp) and 46.80 mg/100 g (seed). Phytosterol analysis revealed β-sitosterol as the dominant sterol, with higher total phytosterol content observed in the seed. The presence of these bioactive lipid components suggests that jackfruit pulp and seed possess significant nutritional and functional potential. The pulp oil, in particular may serve as a valuable source of heart-healthy unsaturated fatty acids, while the seed oil may have potential applications in functional foods and nutraceutical formulations due to its phytosterol content. These findings highlight the importance of promoting the utilization of jackfruit as a sustainable food resource with potential health benefits. However, phosphatidylserine levels in both samples were below the recommended USFDA standard.
AGRO-WASTE FILLERS: IMPACT ON MECHANICAL, MORPHOLOGICAL, SOLVENT IMBIBITION AND DEGRADATION PROPERTIES OF LOW DENSITY POLYETHYLENE
This research is aimed at studying the impact of agro-waste filler on the mechanical, morphological, solvent imbibition and degradation properties of low density polyethylene. The fine agro waste of cassava peel-cowpea hull of 75μm particle size was blended in a 1:1 ratio then integrated into LDPE matrix at different weight percentages (0, 5, 10, 15, and 20) and composites fabricated via injection moulding technique. The mechanical properties; tensile strength, percentage elongation at break, compressive strength, shear modulus and hardness test were studied in line with ASTM standards. The morphological property was studied with Scanning Electron Microscope. Degradation study was done using soil burial method for 180 days. The mechanical properties results showed that there was an increase in tensile strength, percentage elongation at break, hardness and shear modulus of the composites with increasing filler loading. The compressive strength decreased with increasing filler loading. Morphological study of the composites showed a good adhesion and interfacial bonding between the filler and the polymer matrix due to good dispersion of the fillers in the polymer matrix. Degradation test showed a decrease in the mass of the composites after 180 days burial period indicating that the composite is degradable. The solvent imbibition test showed no increase in the mass of the composites after immersion in water for 72 hours at room temperature, indicating that the composite can be utilized in wet environments. Thus, from the findings, it is suggested that agro-wastes such as cassava peel and cowpea hull be employed as fillers in the manufacture of plastics like packaging containers, shoe soles. This is because the fillers are cheaper and viable and the produced plastics would degrade when discarded.
A MODIFIED RELAXED CQ ALGORITHM FOR THE SOLUTION OF MULTIPLE SPLIT FEASIBILITY PROBLEM IN REAL HILBERT SPACES
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height:15pt'> <v:imagedata src="file:///C:/Users/HP/AppData/Local/Temp/msohtmlclip1/01/clip_image001.png" o:title="" chromakey="white"/> </v:shape><![endif]--> be Hilbert spaces, let <!-- [if gte msEquation 12]><m:oMath><m:sSub><m:sSubPr><m:ctrlPr></m:ctrlPr></m:sSubPr><m:e><m:r>C</m:r></m:e><m:sub><m:r>i</m:r></m:sub></m:sSub><m:r>, </m:r><m:sSub><m:sSubPr><m:ctrlPr></m:ctrlPr></m:sSubPr><m:e><m:r>K</m:r></m:e><m:sub><m:r>i</m:r></m:sub></m:sSub><m:r> </m:r><m:d><m:dPr><m:ctrlPr></m:ctrlPr></m:dPr><m:e><m:r>i</m:r><m:r>=1,2, …,</m:r><m:r>m</m:r></m:e></m:d></m:oMath><![endif]--> <span style="font-size: 11.0pt; line-height: 107%; font-family: 'Calibri',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; position: relative; top: 4.0pt; mso-text-raise: -4.0pt; mso-font-kerning: 0pt; mso-ligatures: none; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;"><!-- [if gte vml 1]><v:shape id="_x0000_i1025" type="#_x0000_t75" style='width:104.25pt;height:15pt'> <v:imagedata src="file:///C:/Users/HP/AppData/Local/Temp/msohtmlclip1/01/clip_image003.png" o:title="" chromakey="white"/> </v:shape><![endif]--> be closed convex nonempty subsets of <!-- [if gte msEquation 12]><m:oMath><m:sSub><m:sSubPr><m:ctrlPr></m:ctrlPr></m:sSubPr><m:e><m:r>X</m:r></m:e><m:sub><m:r>i</m:r></m:sub></m:sSub></m:oMath><![endif]--> <span style="font-size: 11.0pt; line-height: 107%; font-family: 'Calibri',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; position: relative; top: 4.0pt; mso-text-raise: -4.0pt; mso-font-kerning: 0pt; mso-ligatures: none; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;"><!-- [if gte vml 1]><v:shape id="_x0000_i1025" type="#_x0000_t75" style='width:11.25pt;height:15pt'> <v:imagedata src="file:///C:/Users/HP/AppData/Local/Temp/msohtmlclip1/01/clip_image005.png" o:title="" chromakey="white"/> </v:shape><![endif]--> and <!-- [if gte msEquation 12]><m:oMath><m:r> </m:r><m:sSub><m:sSubPr><m:ctrlPr></m:ctrlPr></m:sSubPr><m:e><m:r>Y</m:r></m:e><m:sub><m:r>i</m:r></m:sub></m:sSub></m:oMath><![endif]--> <span style="font-size: 11.0pt; line-height: 107%; font-family: 'Calibri',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; position: relative; top: 4.0pt; mso-text-raise: -4.0pt; mso-font-kerning: 0pt; mso-ligatures: none; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;"><!-- [if gte vml 1]><v:shape id="_x0000_i1025" type="#_x0000_t75" style='width:12pt;height:15pt'> <v:imagedata src="file:///C:/Users/HP/AppData/Local/Temp/msohtmlclip1/01/clip_image007.png" o:title="" chromakey="white"/> </v:shape><![endif]--> respectively and let <!-- [if gte msEquation 12]><m:oMath><m:sSub><m:sSubPr><m:ctrlPr></m:ctrlPr></m:sSubPr><m:e><m:r><m:rPr><m:scr m:val="roman"/><m:sty m:val="p"/></m:rPr> </m:r><m:sSub><m:sSubPr><m:ctrlPr></m:ctrlPr></m:sSubPr><m:e><m:r>A</m:r></m:e><m:sub><m:r>i</m:r></m:sub></m:sSub><m:r>:</m:r><m:r>X</m:r></m:e><m:sub><m:r>i</m:r></m:sub></m:sSub><m:r> →</m:r><m:sSub><m:sSubPr><m:ctrlPr></m:ctrlPr></m:sSubPr><m:e><m:r>Y</m:r></m:e><m:sub><m:r>i</m:r></m:sub></m:sSub><m:r> </m:r><m:d><m:dPr><m:ctrlPr></m:ctrlPr></m:dPr><m:e><m:r>i</m:r><m:r>=1,2, …,</m:r><m:r>m</m:r></m:e></m:d></m:oMath><![endif]--> <span style="font-size: 11.0pt; line-height: 107%; font-family: 'Calibri',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; position: relative; top: 4.0pt; mso-text-raise: -4.0pt; mso-font-kerning: 0pt; mso-ligatures: none; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;"><!-- [if gte vml 1]><v:shape id="_x0000_i1025" type="#_x0000_t75" style='width:137.25pt;height:15pt'> <v:imagedata src="file:///C:/Users/HP/AppData/Local/Temp/msohtmlclip1/01/clip_image009.png" o:title="" chromakey="white"/> </v:shape><![endif]--> be bounded linear operators. The problem of finding <!-- [if gte msEquation 12]><m:oMath><m:sSub><m:sSubPr><m:ctrlPr></m:ctrlPr></m:sSubPr><m:e><m:sSub><m:sSubPr><m:ctrlPr></m:ctrlPr></m:sSubPr><m:e><m:r>x</m:r></m:e><m:sub><m:r>i</m:r></m:sub></m:sSub><m:r> </m:r><m:r>ϵ</m:r><m:r> </m:r><m:r>C</m:r></m:e><m:sub><m:r>i</m:r></m:sub></m:sSub></m:oMath><![endif]--> <span style="font-size: 11.0pt; line-height: 107%; font-family: 'Calibri',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; position: relative; top: 4.0pt; mso-text-raise: -4.0pt; mso-font-kerning: 0pt; mso-ligatures: none; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;"><!-- [if gte vml 1]><v:shape id="_x0000_i1025" type="#_x0000_t75" style='width:31.5pt;height:15pt'> <v:imagedata src="file:///C:/Users/HP/AppData/Local/Temp/msohtmlclip1/01/clip_image011.png" o:title="" chromakey="white"/> </v:shape><![endif]--> such that <!-- [if gte msEquation 12]><m:oMath><m:sSub><m:sSubPr><m:ctrlPr></m:ctrlPr></m:sSubPr><m:e><m:r><m:rPr><m:scr m:val="roman"/><m:sty m:val="p"/></m:rPr> </m:r><m:sSub><m:sSubPr><m:ctrlPr></m:ctrlPr></m:sSubPr><m:e><m:r>A</m:r></m:e><m:sub><m:r>i</m:r></m:sub></m:sSub><m:r>x</m:r></m:e><m:sub><m:r>i</m:r></m:sub></m:sSub><m:sSub><m:sSubPr><m:ctrlPr></m:ctrlPr></m:sSubPr><m:e><m:r> </m:r><m:r>ϵ</m:r><m:r> </m:r><m:r>K</m:r></m:e><m:sub><m:r>i</m:r></m:sub></m:sSub><m:r><m:rPr><m:scr m:val="roman"/><m:sty m:val="p"/></m:rPr> </m:r><m:d><m:dPr><m:ctrlPr></m:ctrlPr></m:dPr><m:e><m:r>i</m:r><m:r>=1,2, …,</m:r><m:r>m</m:r></m:e></m:d></m:oMath><![endif]--> <span style="font-size: 11.0pt; line-height: 107%; font-family: 'Calibri',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; position: relative; top: 4.0pt; mso-text-raise: -4.0pt; mso-font-kerning: 0pt; mso-ligatures: none; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;"><!-- [if gte vml 1]><v:shape id="_x0000_i1025" type="#_x0000_t75" style='width:124.5pt;height:15pt'> <v:imagedata src="file:///C:/Users/HP/AppData/Local/Temp/msohtmlclip1/01/clip_image013.png" o:title="" chromakey="white"/> </v:shape><![endif]--> is called the multiple split feasibility problem (MSFP). We introduced the multiple split feasibility problem (MSFP) as a natural generalization of the classical split feasibility problem and modify the relaxed CQ algorithm for solution of the split feasibility problem (SFP) for the iterative solution of the MSFP. We establish the weak convergence of this modified algorithm under very mild conditions on the iterative parameters. Using an Ishikawa like process, strong convergence is established.
HARNESSING CLEAN ENERGY SECURELY: THE DUAL ROLE OF CYBERSECURITY AND PHYSICAL SCIENCES IN ECONOMIC RENEWAL
Transitioning into clean energy is imperative for the regeneration of the economy and sustainability. Technological development in physical sciences fortified clean energy technology, but the challenge of cybersecurity is paramount. The study detects the convergence of physical sciences, cybersecurity, and clean energy. Mixed-methods methodology was adopted for the study analysis. The development of robust and fortified clean energy technology requires the application of physical sciences, investments in cybersecurity infrastructure, clean energy technology, and population training are essential to the economy's recovery. The development of the standards and guidelines in the area of cybersecurity in clean energy infrastructure, and the development of cooperation among the stakeholders, is favorable. The use of clean energy in the manner that is secure, can boost the development of the economy, reduce the levels of carbon emissions, and sustainably construct the future. The integration of cybersecurity and physical sciences is essential for the protection of clean energy and economic recovery. The study concludes that the incorporation of cybersecurity measures in the clean energy infrastructure is vital in the defense against disruption and security against cyberattacks. The study serves as the foundation upon which more research can be done and policies established. The findings in this study are generalizable in the area of policymakers, the leaders in the sectors, and the researchers that are interested in sustainable development and the security of clean energy.
MAPPING MINERALIZED POTENTIAL AREAS USING INTEGRATED GEOPHYSICAL AND GEOCHEMICAL TECHNIQUES IN PART OF ILORIN SHEET 223, SOUTHWESTERN NIGERIA
This study employs an integrated geophysical and geochemical approach to delineate mineralized zones in part of Ilorin Sheet 223, southwestern Nigeria. Advanced filtering and transformation techniques were applied to the high-resolution aeromagnetic data, this includes reduction to the equator (RTE), analytic signal amplitude (AS), tilt derivative (TDR), total horizontal derivative (THDR), first vertical derivative (FVD), source parameter imaging (SPI), and Euler deconvolution techniques, revealing a structurally complex terrain dominated by NE-SW and NW-SE lineaments, shallow magnetic sources (60–350 m depth). Aeroradiometric analysis, which consist of ternary maps and K/Th and K/U ratios, highlighted potassic alteration and radioelement enrichments associated with felsic granites and pegmatites. Soil geochemical analysis of 49 samples for 13 trace elements (Li, Rb, Cs, Nb, Ta, Sn, Cu, U, Th, Pb, Zn, Fe, La) using XRF, analyzed with statistical analysis including univariate and multivariate statistics, identified log-normal distributions, element correlation that reveal strong element associations, and three principal factors were observed: (1) Fe-Cu-Zn-U (hydrothermal/supergene), (2) Li-Cs-Nb-Ta-Sn-Rb (LCT pegmatite-related), and (3) Th-La-Pb-U (REE-radiogenic). Integration of these datasets delineates high-priority polymetallic targets in the northeastern (Elesin-Funfun/Alayin), western (Oniguguru/Oniyere/Ahogbada), and southeastern (Erin-Ile) sectors, controlled by structural intersections and hydrothermal alteration. The results provide a robust, multi-parameter exploration model that reduces target ambiguity and highlights the prospectivity for polymetallic mineralization, supporting sustainable resource development in the study area.
A Confounding Plans in a 2K Facorial Design
When the number of treatments is greater than the available blocks in 2k factorial experiments confounding becomes necessary to reduce the block size as well as reduce experimental errors. It is also unavoidable when the treatments are greater than the block size. 2k factorial confounding plans were studied with k = 2, 3, 4 and k > 4. Confounding when k = 2 and 3 is not necessary as the treatment combinations are not much. However, from k > 4, confounding becomes necessary as the treatment combinations are many. From our confounding plans, it is only in 24 that all the main effects are found in one block when confounded with ABCD. The result is not the same with k > 4. Therefore, when many factors are needed is an experiment, the 24 factorial experiment is recommended.
Application of Electrical Method for Geophysical Assessment of Subsurface Structures Contributing to Road Failures in Shao-Malete Road, Kwara State
This study applies the Electrical Resistivity Method using the Vertical Electrical Sounding (VES) technique to investigate and evaluate subsurface structures contributing to recurrent road failures along the Shao-Malete Road in Kwara State, Nigeria. A total of seventy-one (71) VES stations were conducted using the Schlumberger array to delineate the subsurface lithological variations and assess their influence on pavement stability. The interpretation resistivity data revealed a different subsurface layer’s sequence consisting of lateritic topsoil, clayey-sand, sandy-clay, weathered basement and fresh basement. The reveal that the lateritic topsoil was found to be thin or discontinuous in several failed segments of VES 6-10, 12, 14, 16 and 21 locations, making these areas vulnerable to deformation under vehicular loads. The clayey and sandy-clay layers is also characterized by low to moderate resistivity values identified at shallow depths in VES 5, 11, 22, 35, 42, 51, 52, 58, and 62 locations, which indicate high moisture retention and swelling potential that further contribute to pavement weakening during the wet season. In addition, a thick and poorly consolidated weathered basement layer was delineated beneath some failed segments, contributing to reduced subgrade strength and the development of pavement distress such as cracks and potholes. This study demonstrates that subsurface heterogeneity and moisture-sensitive materials play significant roles in the observed road failures. It recommends integrating geophysical surveys in preconstruction assessments to guide subgrade stabilization and drainage design for sustainable road infrastructure.
Groundwater Investigation using Dar Zarrouk Parameters for Hydrogeological Subsurface Assessment in Niger State Polytechnic, Zungeru
Groundwater constitutes the principal source of water for domestic and institutional use at Niger State Polytechnic, Zungeru, necessitating a detailed evaluation of its hydrogeological potential. This study employed Dar Zarrouk parameters derived from Vertical Electrical Sounding (VES) data to characterize the subsurface and assess aquifer properties within the Polytechnic environment. A total of forty (40) VES stations were acquired using the Schlumberger array, and data interpretation through curve matching and computer iteration delineated subsurface layers and aquifer characteristics. Results identified three to four lithological units: topsoil/lateritic cover, clay, sandy clay, clayey sand, weathered basement, and fresh basement. Dar Zarrouk parameters, such as longitudinal conductance, transverse resistance, hydraulic conductivity, transmissivity, reflection coefficient, and anisotropy, provided insights into aquifer protective capacity and groundwater potential. Aquifer resistivity ranged from 2-402 Ωm, with thicknesses of 0.6-19.6 m, while basement resistivity exceeded 1,000 Ωm, confirming a crystalline bedrock. High transmissivity and hydraulic conductivity at VES 1, 6, 11, 25, and 36 denote productive aquifers, whereas clayey zones showed poor recharge. Groundwater occurrence is predominantly associated with weathered and fractured basement zones, which constitute the main aquifers. It is recommended that borehole development target these zones to ensure a sustainable water supply for the Polytechnic and surrounding communities.
Mathematical Model for the dynamics of Kidnapping in Nigeria with Optimal Control
Kidnapping is regarded as one of the most terrifying types of banditry in the world. In Nigeria, it is a volent act use for perpetuating political objectives and interest by politicians. We developed a compartmentalized mathematical model for the dynamics of kidnapping activity. Total human population was sub-divided into S(t), I(t), U(t), R(t), K(t) and D(t), a system non-linear first order ordinary differential equations was developed. Two equilibrium points were established (kidnapper free equilibrium point and equilibrium point with kidnapping activity), stability analysis was carried out and the analytical result shows that the two equilibrium points are local asymptotically stable (LAS). The equilibrium point with kidnapping activity is globally asymptotically stable (GAS) provided that the condition of the Lyapunov function is satisfied. Additionally, the fundamental reproduction number or threshold number was calculated followed by numerical simulations to back up the analytical results.
A DEEP LEARNING-BASED MODEL FOR MAIZE DISEASE CLASSIFICATION
Food security has remained an urgent need for a suitable life all over the world. This study focuses on maize, one of the widely cultivated staple crops, and proposes a solution to address low yields. The aim is the application of a deep learning-based model for maize disease classification. The methodology adopted is the Rapid Application Development (RAD) approach. The data used for this work were collected from three maize farms in Nsukka, Enugu State, Nigeria. The sample size of the collected data consisted of 12,032 images of maize leaves with diseases across eight classes (Yellow curl, Septoria, Gray leaf spot, Healthy, Mold, Bacterial soft rot, Mosaic virus, Late blight). The data were augmented using the PlantVillage dataset as the secondary source. Both data sets formed the new data model for maize disease detection. This was used to fine-tune DenseNet121, a pre-trained algorithm, and generate a maize disease classification model. The Python programming language was used for the implementation. Metrics such as accuracy, precision, recall, and F1 score were applied to evaluate the model's performance. The model achieved an accuracy of 95%, with precision, recall, and F1-scores consistently above 93%. Finally, the findings from the study confirmed that the DenseNet121-based applications offer a pathway to the mitigation of losses in maize yield, thereby strengthening food security and empowering smallholder farmers with precision agriculture tools. In addition to its scientific contribution, this study addresses a sustainable development goal of zero hunger. This is achieved by providing a user-friendly mobile-based precision agriculture application that supports real-time disease detection, reduces crop losses, and empowers farmers to adapt more effectively to the challenges of climate change and food insecurity.
SIMULATING NATIONAL GAINS THROUGH EDUCATION: A DIAGNOSTIC FRAMEWORK ANCHORED IN SDG 4 AND THE PHYSICAL SCIENCESS
The physical sciences—comprising physics, chemistry, earth sciences, and astronomy—form the bedrock of scientific literacy. Quality education, as articulated in the United Nations Sustainable Development Goal 4 (SDG 4), extends beyond access to schooling to embrace inclusion, equity, and lifelong learning. Mastery of the physical sciences cultivates analytical reasoning, data interpretation, and evidence-based decision-making—competencies that are transferable across disciplines and indispensable for sustainable development. This paper presents a simulation-based diagnostic framework that quantifies the relationship between educational quality and national development. Using normalized data from 101 countries, the model constructs composite performance measures and explores their distribution through scenario-oriented analysis. Results demonstrate wide cross-national variation in education-driven national gains, with most countries clustered in a mid-range performance band. A comparative assessment highlights Nigeria’s current position relative to peer and benchmark countries and illustrates the potential for upward mobility through targeted education reforms. The approach provides a practical, data-driven tool for policymakers seeking to align investment in science education with national development outcomes.
A Comparative Analysis on diabetes using Ensemble Machine Learning Models on Pima Indian Datasets
Diabetes is a major global health concern, affecting millions worldwide and leading to severe health complications if not detected early. Timely and accurate diabetes prediction can greatly enhance patient outcomes. We suggest a diabetes prediction system in this article that uses a number of machine learning (ML) models, such as Logistic Regression, Random Forest, Support Vector Machine, and XGBoost. The models were evaluated using the Pima Indians Diabetes Dataset. Accuracy, precision, recall, F1-score, and The receiver operating characteristic (ROC) curve's area under the curve (ROC) metrics were used to evaluate performance. Our findings reveal that ensemble methods like Random Forest and XGBoost outperformed traditional classifiers, achieving prediction accuracy of above 88.0%. This work highlights the potential of machine learning models in the early detection of diabetes and provides insights for developing scalable, real-time prediction systems.
EVALUATION OF SUBSURFACE STRUCTURES USING HIGH RESOLUTION AEROMAGNETIC DATASET FOR HYDROCARBON POTENTIAL PROSPECTIVITY IN PARTS OF THE ANAMBRA AND SOUTHERN BIDA BASINS, NIGERIA
This study focuses on the evaluation of subsurface structures using high resolution aeromagnetic dataset for hydrocarbon potential prospectivity in parts of the Anambra and Southern Bida Basins, Nigeria. Four high-resolution aeromagnetic data were collected, analyzed and evaluated to interpret the structural framework, assess basement topography, and evaluate the hydrocarbon potential of the study region. The datasets were interpreted using a combination of geophysical interpretation tools such as Oasis Montaj, Surfer, and Geo Rose software. Results from qualitative analysis of the residual anomaly map (RAM), the reduce to pole (RTP) map and the rose diagram reveals significant northeast to southwest (NE-SW) trending faults within the region, reflecting the regional tectonic regime. This conforms to the trend of the Benue Trough. The result also reveals evidence of other structural feature such as folds in the area. These faults form potential migration pathways and trapping mechanisms for hydrocarbon accumulation. Lineament analysis as reveal by the Analytical signal map showed dense fault networks predominately around the southern area, with prominent basement depressions that could serve as accommodation spaces for sediments. The identified structural traps, coupled with the thick sedimentary cover and fault-controlled migration pathways, suggest that the study area is prospective for hydrocarbon accumulation. The sedimentary thickness obtained through Spectral analysis ranges from 0.42 km – 5.24 km with average of 1.72 km across the study area. The deep sedimentary cover signifying deep depth to magnetic sources vary from 2.03km to 5.24 km, whereas the shallow sedimentary cover (shallow depth to magnetic sources) varies from 0.41 km to 1.85 km. The sedimentary cover map reveals deeper sedimentary cover in the southern and central parts of the study area while, the shallower sedimentary cover occurs in the northern parts. The result of the estimated oil and gas window is between 74.78 °C and 150.21 °C across the study area. The study concludes that there is likelihood of hydrocarbon generation in the southern and central regions of the study area, whereas, the northern and other parts of the study area appear plausible for mineral exploration.
Usman, Ayatu Ojonugwa Usman, Ayatu Ojonugwa