Indexing

Abstract: This study assessed the potential health risks associated with the transfer of heavy metals from contaminated soils to food crops in the Pb-Zn mining communities in Ebonyi State, Nigeria. Soil and plant samples of Telfairia occidentalis, Manihot esculenta, and Oryza sativa were collected from Enyigba, Ameri, Ameka, and Ishiagu mining districts in Ebonyi State and analyzed for the concentration of Pb, Zn, Cu, Cd, and Cr using the Atomic Absorption Spectrophotometer technique. Transfer Factors, Daily Intake of Metals, and Health Risk Index were used to evaluate the potential health hazards in the study area. The results indicated that Zinc has the highest Transfer Factor, ranging from 1.0 to 2.0, especially in pumpkin, Telfairia occidentalis, with a concentration of 5.9 ppm. The results of the Daily Intake of Metals indicated that the intake of Zinc, Lead, and Cadmium by children in the study area was 6.9×10⁻⁴ mg/kg/day, 6.24×10⁻⁴ mg/kg/day, and 9.4×10⁻⁶ mg/kg/day, respectively, in the contaminated vegetable samples. Similarly, the results of the Health Risk Index indicated that the potential health hazard in the study area ranges from 0.000221 to 0.023 for Zinc, 0.031 to 0.176 for Lead, and 0.00949 to 0.0232 for Cadmium, all below the threshold value of 1, indicating that the study area is safe, but the cumulative effect of the exposure and the presence of toxic metals in the edible parts of the plant require serious public health concern.

Keywords: health risk assessment, transfer factor, daily intake of metals, heavy metals, food crops, bioaccumulation, Nigeria.

Title: Health Risk Assessment of Heavy Metal Transfer from Contaminated Soils to Food Crops in Pb-Zn Mining Communities of Southeastern Nigeria

Author: Osayande A.D.

International Journal of Recent Research in Interdisciplinary Sciences (IJRRIS)

ISSN 2350-1049

Vol. 13, Issue 1, January 2026 - March 2026

Page No: 59-64

Paper Publications

Website: www.paperpublications.org

Published Date: 26-February-2026

Abstract: Access to safe and sustainable drinking water is a critical challenge in the Obio-Akpor Local Government Area, where rapid population growth and industrial activities have increased the risk of groundwater contamination. The primary objective of this study was to employ geophysical techniques to delineate potential freshwater aquifers within the school's premises. The study aimed to determine the subsurface lithology, overburden thickness, and water table distribution to guide the successful drilling of a potable water borehole. A geophysical survey was conducted using the Electrical Resistivity Method, specifically the Vertical Electrical Sounding (VES) technique with a Schlumberger electrode configuration. Data were acquired using an ABEM Terrameter (e.g., SAS 1000 or 300 models), which measured the resistance of subsurface layers. Interpretation of the VES data revealed multiple geo-electric layers, with topsoil, clayey sand, and prolific sand aquifers. Regional data suggests the water table in Obio-Akpor typically ranges from 1.6 to 9.0 metres, though deeper drilling is often required to reach uncontaminated potable sources. The survey identified the most viable borehole locations by pinpointing high-resistivity zones indicative of freshwater-bearing sands while avoiding potential saltwater intrusion zones. The use of a digital Terrameter provided a cost-effective and non-invasive means of aquifer delineation at CSS Nkpolu. The study recommends drilling to the identified deep-seated aquiferous zones to ensure a long-term supply of water that meets the Nigerian Standard for Drinking Water Quality (NSDWQ).

Keywords: Potable Water, Digital Terrameter, Vertical Electrical Sounding (VES), Aquifer Delineation, Obio-Akpor LGA, Rivers State.

Title: SUBSURFACE MAPPING FOR GROUNDWATER PORTABILITY AT COMMUNITY SECONDARY SCHOOL NKPOLU: A DIGITAL TERRAMETER APPROACH

Author: Sam, Edikan Michael, Njoku, Chibueze

International Journal of Recent Research in Interdisciplinary Sciences (IJRRIS)

ISSN 2350-1049

Vol. 13, Issue 1, January 2026 - March 2026

Page No: 53-58

Paper Publications

Website: www.paperpublications.org

Published Date: 20-February-2026

Abstract: Contemporary Artificial Intelligence (AI) serves as an advanced and transformative tool in bio-informatics, advancing the discipline past conventional, rule-following computing techniques to manage the rapid increase and complexity of biological information. By employing machine learning (ML), deep learning (DL), and natural language processing (NLP), AI offers automatic feature identification and enhanced predictive ability in fields like genomics, predicting protein structures, and discovering new drugs. AI is transforming bio-informatics by offering strong tools for analyzing and interpreting massive volumes of biological data. Machine learning (ML) and deep learning (DL) algorithms are particularly useful for recognizing patterns, making predictions, and addressing complicated bio-informatics problems. This encompasses activities such as gene prediction, protein structure prediction, and drug discovery. This paper examines the crucial bio-informatics evolutionary intervention strategy of contemporary AI. The development of new drugs, therapies, and insights into disease management has been facilitated by the advancement of AI algorithms, which have increased the speed and accuracy of biological data analysis. AI is able to predict outcomes, simulate intricate biological processes, and quickly find patterns and relationships in large datasets. This paper examines the impact of Artificial Intelligence (AI) on Bio-informatics, a field that analyzes biological data using computational tools and techniques. This paper provides an overview of AI methodologies used in Bio-informatics and discusses their impact on biological data analysis, genomics, proteomics, and drug discovery. Bio-informatics will be significantly impacted by AI's continued development, which will also hasten advancements in the healthcare and medical industries. Modern artificial intelligence (AI) has fundamentally changed how we think about bio-informatics and is a critical intervention tool for locating and evaluating biological data. Researchers can now process enormous amounts of biological data with unprecedented speed and accuracy thanks to artificial intelligence (AI), which enables them to create more efficient diagnostic and therapeutic approaches. Additionally, AI-based tools can aid in the development of novel medications and treatments, enabling the field of bio-informatics to develop and significantly advance medical science.

Keywords: Artificial intelligence, algorithms, analyze, biological, bio-informatics, diagnostic, data, disease, drug, medical, medical science.

Title: CONTEMPORARY ARTIFICIAL INTELLIGENCE AS A PROGRESSIVE INTERVENTION STRATEGY FOR BIO-INFORMATICS

Author: Princewill Chinomso Okeke

International Journal of Recent Research in Interdisciplinary Sciences (IJRRIS)

ISSN 2350-1049

Vol. 13, Issue 1, January 2026 - March 2026

Page No: 42-52

Paper Publications

Website: www.paperpublications.org

Published Date: 07-February-2026

Abstract: This study investigated the application of digital twin frameworks to pharmaceutical product lifecycle management (PLM) with the objective of improving forecasting accuracy, regulatory readiness, and commercial decision-making across clinical, regulatory, and market domains. A modular digital twin architecture was developed to represent pharmaceutical products as dynamic, state-dependent systems integrating clinical performance indicators, regulatory progression variables, and market response signals within a unified simulation environment. Using coupled state-space modeling, Monte Carlo simulation, and sensitivity analysis, the framework evaluated lifecycle trajectories under alternative strategic scenarios, including regulatory delays, clinical failure probabilities, pricing adjustments, and competitive entry dynamics. Comparative analysis against conventional PLM approaches demonstrated substantial reductions in forecast error, decision latency, and lifecycle value volatility, particularly during regulatory review and post-market phases. The digital twin framework attenuated regulatory and clinical risk exposure by enabling early detection of high-impact uncertainties and proactive mitigation through adaptive evidence-generation and commercialization strategies. Commercial optimization outcomes showed consistent improvements in pricing realization, launch timing, market access sequencing, and portfolio reallocation, resulting in higher risk-adjusted net present value across lifecycle phases. The findings confirmed that integrated lifecycle simulation transforms commercialization from a downstream, reactive process into a continuous, data-driven optimization function. Overall, the study established digital twins as strategic decision infrastructures capable of synchronizing scientific, regulatory, and commercial objectives, offering a scalable approach to managing complexity and uncertainty in modern pharmaceutical development and commercialization.

Keywords: Digital Twins; Pharmaceutical Product Lifecycle Management; Regulatory Simulation; Clinical Dynamics Modeling; Commercial Optimization.

Title: Digital Twins for Pharmaceutical Product Lifecycle Management: Simulating Market, Regulatory, and Clinical Dynamics for Commercial Optimization

Author: Ezichi Adanna Anokwuru, Joy Onma Enyejo

International Journal of Recent Research in Interdisciplinary Sciences (IJRRIS)

ISSN 2350-1049

Vol. 13, Issue 1, January 2026 - March 2026

Page No: 27-41

Paper Publications

Website: www.paperpublications.org

Published Date: 31-January-2026

DOI: https://doi.org/10.5281/zenodo.18440994

Abstract: Educational inequality remains a persistent and systemic challenge in low-resource school systems, where limited instructional capacity, unstable learning environments, and delayed identification of academic difficulties contribute to widening learning gaps. In such contexts, traditional assessment practices often detect failure only after significant learning loss has occurred, reducing the effectiveness of remedial interventions. This study proposes a predictive analytics framework that applies machine learning techniques to proactively identify, characterize, and prioritize learning gaps in low-resource schools, with a specific focus on supporting equity-driven educational decision-making. The framework integrates multi-source educational data, including academic performance records, attendance histories, and engagement indicators, to capture both instructional outcomes and structural conditions influencing learning. Feature engineering emphasized temporal and behavioral instability measures such as attendance entropy, assessment decay, and engagement volatility, reflecting patterns commonly associated with sustained underperformance in underserved settings. Multiple machine learning models were evaluated, including logistic regression, random forest, gradient boosting, and long short-term memory architectures. Model evaluation prioritized equity-sensitive metrics, particularly recall and F1-score, to minimize false negatives and ensure early identification of at-risk learners.

Results indicate that ensemble-based models, especially gradient boosting, achieved the highest predictive performance, with strong recall and balanced precision across learning-gap categories. Attendance-linked and engagement-driven gaps emerged as the most severe and prevalent, while foundational literacy and numeracy gaps showed comparatively lower volatility. Equity-stratified analysis revealed a clear socioeconomic gradient, with learners in low-resource contexts experiencing disproportionately higher learning-gap intensity across all dimensions. To operationalize predictive insights, the study developed a targeted intervention prioritization framework that aligned learning-gap structure with intervention type, cost, and time-to-effect. Findings suggest that low-cost stabilization strategies, such as attendance nudges and engagement coaching, yield the greatest short-term equity gains, while intensive remediation is most effective when selectively applied. Overall, the study demonstrates that machine learning-driven predictive analytics can enable proactive, scalable, and equity-focused interventions in low-resource educational systems.

Keywords: Predictive Analytics; Educational Equity; Machine Learning; Learning Gaps; Low-Resource Schools.

Title: Predictive Analytics for Educational Equity: A Machine Learning Approach to Identifying Learning Gaps in Low-Resource Schools

Author: Linda Aluso, Stella Awo Kpogli, Joy Onma Enyejo

International Journal of Recent Research in Interdisciplinary Sciences (IJRRIS)

ISSN 2350-1049

Vol. 13, Issue 1, January 2026 - March 2026

Page No: 12-26

Paper Publications

Website: www.paperpublications.org

Published Date: 27-January-2026

Abstract: Waste is unwanted or unusable materials discarded after use whose uses is yet unknown. This comprises of domestic, municipal, Industrial, medical and nuclear and radio nuclear wastes. Scrap is a metallic waste that has found its way in recycling for economic purpose.

Scrapyard activities are critical to metal recycling and waste management but are often associated with environmental degradation, particularly soil contamination and potential groundwater pollution. This study investigates the impact of scrapyard operations on soil quality and groundwater contamination in Obio/Akpor Local Government Area, Rivers State, Nigeria, with field samples collected from Eliozu, Alakahia (First Mechanic), Choba, and Aluu. Fifteen (15) samples were collected, treated and analyse to determine soil physicochemical properties, including pH, electrical conductivity, water-holding capacity, organic carbon, and heavy metal concentrations (e.g., Pb, Cd, Fe). The results revealed that the soil pH ranged from 6.24 to 7.57, indicating neutral to slightly acidic conditions, while electrical conductivity varied significantly between 19 µS/cm and 857 µS/cm, reflecting localized salinity differences. Elevated concentrations of heavy metals were observed, particularly Lead (Pb), with values up to 63.156 mg/kg, and Cadmium (Cd), reaching 0.125 mg/kg in certain hotspots, exceeding permissible limits for safe soil use. Organic carbon levels ranged from 1.27% to 9.99%, highlighting variability in soil fertility due to anthropogenic activities. Geospatial analysis demonstrated contamination hotspots near scrapyards, with heavy metal concentrations decreasing with distance from these activities. The study concludes that unregulated scrapyard operations are significant contributors to soil contamination, posing risks to environmental quality and public health. This research establishes a baseline for environmental assessments in the region and underscores the need for sustainable waste management practices to mitigate the adverse effects of scrapyard activities. Recommendations include enforcing environmental regulations, implementing soil remediation strategies, and conducting regular monitoring of both soil and groundwater resources.

Keywords: lithological samples, metallic materials scrapyard, geospatial analysis, waste, contamination.

Title: HEALTH RISK ASSESSMENT OF LITHOLOGICAL SAMPLES SOURCED FROM METALLIC SCRAP YARD IN CHOBA AND ENVIRONS IN PORTHARCOURT, NIGERIA

Author: Osayande, A.D, Azubuike-Ijomah, Kelechi

International Journal of Recent Research in Interdisciplinary Sciences (IJRRIS)

ISSN 2350-1049

Vol. 13, Issue 1, January 2026 - March 2026

Page No: 1-11

Paper Publications

Website: www.paperpublications.org

Published Date: 23-January-2026