IPS Journal of Physical Sciences

Hybrid Cloud Security Framework for E-Health Data Protection using Machine Learning and Advanced Encryption Techniques

2026-05-06T07:54:02-06:00

This study presents a hybrid cloud security framework for E-Health data protection that integrates machine learning-based behavioural analysis with advanced encryption techniques. With the use of Agile methodology, the system was developed to iteratively refine the proposed encryption algorithms in order to ensure adaptive functionality of the technique. A primary dataset made up of 15,001 user activity logs was collected from a cloud-based healthcare platform (Anderson Hospital) capturing both legitimate, suspicious or malicious behaviours. Furthermore, the dataset was then pre-processed using missing value imputation, min-max normalization and Principal Component Analysis (PCA) so as to optimize model training process. A Multilayer Perceptron (MLP) neural network was trained for the prediction of user sessions into three categories such as Legitimate, Suspicious or Malicious. The model achieved strong predictive continuous threat score performance with R² = 0.9946, MAE = 0.0689, and MSE = 0.0188, demonstrating a high predictive accuracy. For data protection, AES-128-bit encryption was used for routine access, while a hybrid Advanced Encryption Standard- Rivest–Shamir–Adleman (AES-256+RSA) approach secured high-risk scenarios. Then, the Experimental results show that the hybrid system provides robust security with acceptable processing overhead, ensuring confidentiality, integrity, and secure access control to sensitive health records. It significantly enhances security against key exchange vulnerabilities and interception attacks.The framework demonstrates the feasibility of real-time cloud-based E-Health data protection and provides a practical solution for safeguarding sensitive healthcare information.

Comparative GC–MS Quantification of Bioactive Fatty Acids and Phytochemicals in Seed, Stem, and Root Oils of Ricinus communis

2026-04-20T10:14:17-06:00

This study presents a comparative evaluation of the phytochemical constituents and quantitative fatty acid composition of oils extracted from the seed, stem, and root of Ricinus communis using Gas Chromatography–Mass Spectrometry (GC–MS). Oil extraction was carried out using n-hexane in a Soxhlet apparatus, and yields varied significantly among plant parts, with the seed recording the highest yield (11.88 %), followed by the root (2.28 %) and stem (1.80 %). Quantitative phytochemical analysis revealed that the root oil possessed the highest phenolic (0.224 mg GAE/g) and saponin (1.555 mg DE/g) contents, while tannins were most abundant in the stem (1.839 mg TAE/g). GC–MS analysis, combined with internal standard quantification, identified and quantified major bioactive fatty acids across the samples. The seed oil was dominated by hexadecenoic acid (45.61 %), n-hexadecanoic acid (23.06 %), and oleic acid (4.88 %). In contrast, the stem oil exhibited a high abundance of 9-octadecenoic acid (100 % peak dominance) along with significant levels of flavonoid-related compounds. The root oil was characterized by 6-octadecenoic acid (100 %), 9-octadecanoic acid (28.25 %), and methyl esters (48.86 %). Quantitative estimation indicated that these fatty acids were present in appreciable concentrations (mg/g), suggesting their potential contribution to biological activity. The identified compounds are widely associated with antimicrobial, antioxidant, anti-inflammatory, and anticancer properties. The results demonstrate notable variation in both phytochemical composition and fatty acid profiles among different plant parts, highlighting the complementary therapeutic potential of R. communis oils. This study provides comprehensive quantitative data that support the pharmacological relevance and industrial applicability of Ricinus communis, particularly in pharmaceutical, nutraceutical, and cosmetic formulations.

A Survey of the Availability and Suitability of Mobile Applications for Digital-Skills Learning Among Older Adults in Nigeria

2026-02-21T01:32:20-07:00

Digital skills have become essential for social participation, access to communication services, financial transactions, healthcare management, government services, and broader economic inclusion. Mobile applications present a promising platform for flexible and self-paced digital-skills acquisition; however, most commercially available applications were not developed with ageing learners in mind. This study surveyed the availability and suitability of mobile applications that support digital-skills learning and assessed their responsiveness to age-friendly design principles. A needs-assessment survey was adopted to determine whether existing applications adequately accommodate the sensory and usability needs of older adults, involving structured analysis of ten (10) mobile applications and supplementary interaction feedback from twenty (20) older adults aged 58–79 years. Quantitative findings demonstrate high availability of general-purpose digital-literacy applications but limited evidence of intentional age-friendly design, with most lacking font adjustability, voice assistance, simplified navigation, or cultural and linguistic localization. Qualitative feedback further revealed visual strain, menu anxiety, and uncertainty about interaction steps as barriers to adoption. Only 22.5% of older adults expressed willingness to use the evaluated apps. The study concludes that current applications insufficiently support ageing users and recommends inclusive development guidelines as in interface design, localization strategies, and policy actions to reduce Nigeria’s senior digital-inclusion gap.

Hybrid and Physics-Based Time Series Models for Forecasting Produced Water Quality: A Comparative Study in the Niger Delta

2025-11-23T13:20:25-07:00

Produced water management is one of the major environmental concerns in the Niger Delta, where the operation of oil production generates enormous volumes of effluents with complex chemical characteristics. In this study, hybrid, physics-based, and machine learning models were formulated and compared for the prediction of main produced water quality parameters of pH, Total Dissolved Solids (TDS), Oil and Grease (O&G), Heavy Metal Concentration (HMC), and Chemical Oxygen Demand (COD). Historical monitoring data from 2010 to 2023 were fitted using five types of models: Autoregressive Integrated Moving Average (ARIMA), ARIMA–Long Short-Term Memory (ARIMA–LSTM), Physics-Informed LSTM (PI–LSTM), Random Forest (RF), and a physics-based process model. Model performance was compared using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), coefficient of determination (R²), and probabilistic forecast intervals. Amongst models, the hybrid PI–LSTM consistently performed better than the rest in terms of prediction accuracy (RMSE = 12.6, MAE = 8.8, R² = 0.87) in terms of seasonal variability and long-term dependency capture for all parameters. The physics-based model provided interpretive insights into water–hydrocarbon interactions and production system dynamics. Overall, results indicate that the integration of physical principles into deep learning models enhances predictive performance and interpretability of water quality predictions generated. Results have significant implications for Niger Delta environmental monitoring, regulatory decision-making, and sustainable produced water management.

Development of Arduino-Based Cyclic Voltammetry Analyzer for Semiconductor Growth

2026-05-18T05:56:44-06:00

This paper presents the development of a for use in growing semiconductor thin film materials. The cyclic voltammetry analyzer is a potentiostat designed for the purpose of performing cyclic voltammograms and thin films growth. The cyclic voltammetry analyzer was developed using an Arduino mega microcontroller unit, a 12-bit analog to digital converter, a 4x4 matrix keyboard, a digital liquid crystal display unit, and a data logger. Sets of probes were used in connecting the working electrode and counter electrodes inserted in the prepared electrolytic bath containing the various ions to be deposited to the potentiostat which controls the supplied current to the electrode. The microcontroller was programmed using Arduino C code to apply the potential sweep and record the current response for the cyclic voltammogram of the sample (working electrode). The cyclic voltammograms of zinc sulfide (ZnS), cadmium sulfide (CdS), and aluminum gallium selenide (AlGaSe) were analyzed using current and voltage data from the developed instrument to determine the suitable potential range for electrodepositing the semiconductor. The results obtained in this work showed compatibility with the obtainable results in the literature.

Fitting a Gamma Distribution to the Weights of Akwa Ibom State University Students Using Chi-Square Method

2026-04-26T21:55:52-06:00

This paper fits a gamma distribution to the weights of Students of Akwa Ibom State University using chi-square technique. The weights of 617 students were collected from the Medical Centre of the Akwa Ibom State University Main Campus, Ikot Akpaden, Akwa Ibom State. A chi-square test is used to ascertain whether or not the weights of Students are gamma distributed. The gamma distribution parameters were estimated using maximum likelihood approach. The graphical displays of the simulated and real data with the same parameter value are presented. It was observed from the results that the gamma distribution fits the weights of students of the Akwa Ibom State University at the significance level . The graphs of the weights of students, the simulated weights of students and the gamma densities values also showed a great disparity.

Structural and Variance Properties of Multivariate Autoregressive Models: Empirical Applications to Nigerian Economic Variables

2026-03-05T04:12:27-07:00

Multivariate Autoregressive models are widely used models in the study of economic variables. However, the understanding of the mean and variance parameters of a Multivariate Autoregressive Model enhances the accuracy and reliability of statistical inferences, forecasting precision, and model stability. It provides deeper insights into the model’s dynamic behavior, allowing for better decision-making in economic, financial, and epidemiological applications. Additionally, it aids in selecting the most appropriate model for specific data structures, ensuring robust and consistent predictions. Using the Nigerian macro-economic variables, the paper advances the theoretical and practical understanding of Multivariate Autoregressive Distributed Lag (MARDL) models by systematically analyzing their properties in comparison to Vector Autoregressive (VAR) models. This paper evaluates the mean and variance properties of both models to assess their relative performance. Findings indicate no significant difference in their mean properties; however, a variance analysis reveals that the MARDL model exhibits smaller variances compared to the VAR model. This suggests that the MARDL model offers greater stability, control, and consistency, making it a more reliable tool for prediction and forecasting. The results contribute to the ongoing refinement of multivariate time series modeling and its application in empirical research.

Understanding the Risk of Indoor Residential Radon Exposure and Readiness to Test among Health Workers

2026-01-27T07:39:15-07:00

This research work provides a detailed analysis of a cognitive survey conducted to assess the knowledge, risk perception, and readiness to test for indoor residential radon exposure among health workers in Ondo State, Nigeria. The central objective of this analysis is to synthesize the study's findings into a comprehensive and actionable framework for public health intervention. The investigation reveals a critical paradox: while the surveyed health workers possess a foundational understanding of radon, their overall awareness and, more importantly, their readiness to take preventative action remain insufficient. The most significant finding is a statistically validated, direct correlation between awareness and the willingness to test for radon exposure. This relationship, confirmed by a Pearson Chi-Square test, establishes that education is the most powerful lever for change. The analysis identifies a significant gap between the health workers' willingness to act and their capability to do so. This is not a failure of motivation but rather a systemic breakdown in providing the necessary logistical and financial support. Key barriers include a lack of knowledge regarding testing procedures, the unavailability of test kits, and the associated cost. This suggests that public health efforts must evolve from solely educational campaigns to integrate strategies that also address these structural and financial obstacles. The recommendations presented in this report focus on leveraging health workers as key communicators and empowering them with the resources and training required to serve as effective public health advocates. By addressing both the knowledge deficit and the logistical barriers, it is possible to transform a latent willingness to act into a robust, community-wide movement toward radon risk mitigation.