Communication in Physical Sciences
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<p>Communication in physical Science is a peer reviewed journal published by Faculty of Physical Sciences, University of Nigeria]- Formerly Journal of Physical Sciences</p>University Of Nigeria Nsukkaen-USCommunication in Physical Sciences2645-2448Analysis of The Impact of Climate Change on Meteorological Time-Series Data in Uyo
https://journalcps.com/index.php/volumes/article/view/603
<p><strong>Sunday Emmanson Udoh</strong></p> <p><strong>Received: 23 November 2024/Accepted 28 January 2025</strong></p> <p><strong>The</strong> annual trends of five meteorological variables were analysed for station in Uyo, the Akwa Ibom State capital, Nigeria, controlled by the Nigerian Meteorological Agency (NiMet) from 1972 to 2021. At the 5% statistical significance level, the non-parametric Mann-Kendall and Sen's slope estimator techniques were used to detect if there was a positive or negative trend and the magnitude of the trend in meteorological data. In this study, there was a significant statistically increasing (positive) trend in mean annual rainfall, maximum temperature, and minimum temperature. However, there was a significant statistically decreasing (negative) trend in average annual relative humidity, solar radiation. The magnitudes of the trends were 19.39mm/year, 0.0314<sup>o</sup>C/year, 0.013<sup>o</sup>C/year, -0.104%/year, and -8.78MJ/m<sup>2</sup>/year, for annual rainfall, maximum temperature, minimum temperature, relative humidity and solar radiation, respectively. The rising trends in precipitation, temperature, and runoff in this research area show that this region is subject to climatic variability. The results of the Mann-Kendall and Sen's slope estimator statistical tests revealed the consistency of performance in the detection of the trend for the meteorological variables.</p>Sunday Emmanson Udoh
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2025-02-072025-02-07122Biomarker Potentials of Postmortem Vitreous Biochemical Parameters For Resolving Disputed Causes of Death by Drowning Using Animal Models
https://journalcps.com/index.php/volumes/article/view/600
<p><strong>Communicaton in Physical Sciences, 2025, 12(2) 309-321</strong></p> <p><strong>Authors: Charles German Ikimi, Ijeoma Cynthia Anyaoku and Maryann Nonye Nwafor</strong></p> <p><strong>Received: September 2024/Accepted: 10 January 2024</strong></p> <p>The lack of biomarkers that can effectively reveal the cause of death when the cause of death is disguised constraints and severely limits the verdict of the coroner. This study aims to explore the discriminatory potentials of selected vitreous biochemical parameters in the autopsy of death by drowning and in death disguised as drowning using rabbits. Completely randomized block design (CRBD) was used for this study. 96 male rabbits were used for this research and were structured into four groups of twenty-four rabbits each: two treatment (test) groups and two control groups. In one test group, the death of the experimental subjects was caused by drowning. In the second test group, the death of the experimental subjects was caused by strangulation, thereafter, the dead subjects were drowned as a cover-up of the actual cause of death. The remaining two groups are the baseline controls. After a postmortem interval of twenty-four hours, vitreous samples were obtained from each group of the experimental animals by Coe and Tente’s methods. The samples were analyzed for the levels of Sodium, Potassium, Carbon iv oxide, Chloride and Calcium using ion selective electrode method, while Total protein, Albumin, Globulin, Glucose, Total cholesterol, Triacylglycerol, Urea, Creatinine, Uric acid, Creatine kinase and Lactate dehydrogenase were analyzed using their specific standard methods. Results obtained were then analyzed with Statistical Package for Social Sciences (Version 18-21) and one-way ANOVA (Post Hoc-LSD). The results show that postmortem vitreous levels of Na, K, Cl, Ca, total protein, creatinine, creatine kinase and lactate dehydrogenase differed significantly (P≤0.05) between samples from death caused by drowning and death by strangulation but disguised as drowning. The striking differences in the levels of these notable analytes can be utilized either as primary or confirmatory tests to reveal and discriminate between death disguised as drowning and death by actual drowning.</p>Charles German Ikimi Ijeoma Cynthia AnyaokuMaryann Nonye Nwafor
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2025-01-202025-01-20122Resource Recovery from Maize Biomass for the Synthesis of SiO2 Nanoparticles and Crystallographic Analysis for Possible Applications
https://journalcps.com/index.php/volumes/article/view/594
<p><strong>Communication in Physical Sciences, 2025, 12(2) 276-293</strong></p> <p><strong>Authors: Oluwafisayomi Folorunso and Aniekan Udongwo</strong></p> <p><strong>Received: 12 September 2023/Accepted: 29 December 2024</strong></p> <p>This study demonstrates the successful recovery of silicon dioxide (SiO₂) nanoparticles from maize biomass, highlighting their structural characteristics and environmental implications. Maize waste samples ere obtained as the stocks of the plants after harvesting and employed as precursors for the synthesis of SiO<sub>2</sub> nanoparticles using the sol gel method. The produced nanoparticles were analysed for their crystalline properties using XRD machine. The X-ray diffraction (XRD) analysis indicated some structural features, including deviations in diffraction angles (2θ) from standard Quartz (SiO₂), ranging from -0.01° to +0.14°. At 30.28°, a positive deviation of +0.14° indicated lattice contraction, while a negative deviation of -0.01° at 41.80° suggested lattice expansion. These shifts reflect lattice strain, defects, and quantum confinement effects at the nanoscale. Calculated d-spacing values, derived using Bragg’s equation, further emphasized these structural alterations, with deviations ranging from -0.12 Å to +0.01 Å. Notably, d-spacing at 30.28° (2.95 Å) showed compression by -0.12 Å compared to the reference value (3.35 Å), while at 41.80° (2.16 Å), a slight expansion of +0.01 Å was observed. Phase analysis confirmed the crystalline SiO₂ structure, with close alignment to standard Quartz (JCPDS Card Number 46-1045). The lattice parameters, calculated as a = 8.07 Å and c = 3.83 Å, showed minimal deviation from standard Quartz values (a = 8.14 Å and c = 3.83 Å), with a minor contraction of -0.7 Å for lattice constant a. Texture coefficient (TC) analysis revealed preferential crystallographic orientations, with high TC values at 30.28° (1.1176) and 35.20° (1.0649), indicating enhanced electrical conductivity and surface reactivity along these planes. Conversely, weaker diffraction at 20.24° (TC = 0.5098) reflected structural imperfections and non-uniform alignment. The structural features observed in the synthesized SiO₂ nanoparticles underscore their suitability for diverse applications. High surface reactivity, arising from nanoscale effects and lattice strain, supports their use in environmental remediation, such as water purification and heavy metal adsorption. The preferential crystallographic orientations enhance catalytic activity, while the structural integrity and stability ensure effectiveness in soil stabilization and nutrient delivery in agriculture. Additionally, the nanoparticles exhibit potential for incorporation into optoelectronic devices and green construction materials, improving functionality and sustainability. The untilization of maize biomass in this study reveals a sustainable method for SiO₂ nanoparticle synthesis and also addresses environmental concerns by valorizing agricultural waste. The results emphasize the need to control synthesis parameters to optimize properties for targeted applications, presenting a significant step toward sustainable nanotechnology solutions with positive environmental impacts.</p>Oluwafisayomi FolorunsoAniekan Udongwo
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2025-01-282025-01-28122Cloud Computing and Machine Learning for Scalable Predictive Analytics and Automation: A Framework for Solving Real-world Problems
https://journalcps.com/index.php/volumes/article/view/592
<p><strong>Communication in Physical Sciences, 2025, 12(2) 406-416</strong></p> <p><strong>Authors: David Adetunji Ademilua* and Edoise Areghan</strong></p> <p><strong>Received: 14 November 2024/Accepted: 28 January 2025</strong></p> <p>This study presents a framework for harnessing cloud computing and machine learning (ML) to address real-world challenges in predictive maintenance, anomaly detection, and sentiment analysis. Leveraging cloud platforms such as AWS and Microsoft Azure, the framework processes large-scale datasets, enabling scalable and efficient solutions across various industries. In the predictive maintenance use case, a machine learning model achieved an accuracy of 92%, precision of 89%, recall of 94%, and an F1 score of 91%, demonstrating its capability to predict equipment failures with high reliability. For anomaly detection, network traffic data was analyzed, yielding a precision of 89%, recall of 85%, and an F1 score of 87%, illustrating the model's efficiency in identifying security threats. In the sentiment analysis task, a subset of 100,000 social media posts was processed, revealing that 45% of the posts were classified as positive, 35% neutral, and 20% negative. The high confidence levels in sentiment predictions, ranging from 85% to 98%, underscore the accuracy and effectiveness of the employed natural language processing (NLP) models. The results align with contemporary studies, which highlight the transformative impact of cloud-based ML systems in enhancing operational efficiency, real-time decision-making, and customer satisfaction across diverse domains (Kairo, 2024;Ucaret al., 2026; Hassan et al., 2024). These findings underscore the potential of combining cloud computing with advanced machine learning algorithms to drive automation, reduce operational costs, and optimize business processes in the digital era.</p> <p> </p>David Adetunji AdemiluaEdoise Areghan
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2025-02-042025-02-04122Type I Half-Logistic Exponentiated Kumaraswamy Distribution With Applications
https://journalcps.com/index.php/volumes/article/view/601
<p><strong>Communication in Physical Sciences, 2025, 12(2) 322-336</strong></p> <p><strong>Authors:</strong> Idayat Abubakar Salau, Aminu Suleiman Mohammed*, Hussaini Garba Dikko<br>Received: 13 November 2024/Accepted: 07 January 2025</p> <p>This study introduces the Type I Half-Logistic Exponentiated Kumaraswamy (TIHLEtKw) distribution, a new statistical model designed to provide improved flexibility and accuracy for data modelling across diverse applications. The background of the study highlights the limitations of existing distributions in capturing complex real-world data patterns. The purpose of this work is to develop and characterize the TIHLEtKw distribution, deriving key properties such as the moment generating function, reliability function, hazard function, and quantile function. Additionally, order statistics were explored to understand the behavior of the distribution. Simulation studies demonstrated the efficiency of the maximum likelihood estimators (MLEs) for the parameters of the TIHLEtKw distribution, with mean square error (MSE) values decreasing as sample size increased, indicating the estimators’ consistency. For example, for a parameter set (α = 2, β = 1.5, γ = 1, δ = 2), the MSE decreased from 0.045 for a sample size of 50 to 0.011 for a sample size of 300. The application of the TIHLEtKw distribution to real datasets, including civil engineering data with a skewness of 2.18 and wind speed data with a kurtosis of 3.62, demonstrated its superior fit compared to other models. Metrics such as the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) indicated that the TIHLEtKw distribution outperformed established models like the Kumaraswamy-Kumaraswamy and Weibull-Kumaraswamy distributions, with reductions in AIC of up to 15%. The findings confirm the TIHLEtKw distribution's effectiveness in capturing data variability and complexity, offering a robust tool for statistical modelling. The study concludes that this distribution significantly enhances modelling capabilities, and it is recommended for use in fields such as environmental studies, biomedicine, and finance. Future research could focus on extending the model's applications and optimizing computational methods for parameter estimation.</p>Idayat Abubakar SalauAminu Suleiman MohammedHussaini Garba Dikko
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2025-01-202025-01-20122Evaluation of Laboratory Resources, Practical Integration, and Challenges in Chemistry Education: A Case Study of Federal Government College, Ikot Ekpene
https://journalcps.com/index.php/volumes/article/view/595
<p><strong>Communication in Physical Sciences, 2025, 12(2) 417-425</strong></p> <p><strong>Author: Aniekan Udongwo </strong></p> <p><strong>Received: 12 May 2023/Accepted: 30 January 2025</strong></p> <p>This study evaluates the state of laboratory resources, the integration of practical work, and associated challenges in chemistry education at Federal Government College, Ikot Ekpene, Nigeria. Laboratory resources are crucial in enhancing science education by fostering hands-on learning experiences, which improve student engagement and comprehension of scientific concepts. However, challenges such as insufficient resources, limited teacher training, and poor integration of practical work persist in Nigerian secondary schools. The study employed both survey research and direct observations to gather data from 37 participants, including 5 chemistry teachers and 32 students. A structured questionnaire was administered, and laboratory facilities were observed to assess the availability and adequacy of resources. The data were analyzed using descriptive statistics, and inferential statistics, including ANOVA, t-tests, and hypothesis testing, were conducted to evaluate the differences between various factors influencing laboratory work. The results revealed significant challenges, including inadequate laboratory equipment (45.9%), limited practical work integration into the curriculum (60%), and teacher training issues (37.8%). ANOVA showed a statistically significant difference in the integration of practical work based on teachers' years of experience (p < 0.05), while the t-test revealed a significant impact of laboratory resources on student academic performance (t = 3.78, p < 0.01). Hypothesis testing supported the assertion that better laboratory resources positively influence student engagement and performance. Based on these findings, recommendations are made for improving laboratory facilities, enhancing practical work integration, and addressing training gaps for teachers. The study offers valuable insights for policymakers and educational stakeholders to improve science education at Federal Government College, Ikot Ekpene, and inform curriculum and resource allocation strategies.</p>Aniekan Udongwo
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2025-02-052025-02-05122Conceptual Design Of A Hybrid Deep Learning Model For Classification Of Cervical Cancer Acetic Acid Images
https://journalcps.com/index.php/volumes/article/view/593
<p><strong>Communication in Physical Sciences, 2024, 12(2): 175-193</strong></p> <p><strong>Authors: Fatima Binta Adamu*, Muhammad Bashir Abdullahi, </strong><strong>Sulaimon</strong><strong> Adebayo Bashir, and Abiodun Musa Aibinu</strong></p> <p><strong>Received: 04 November 2024/Accepted: 30 December 2024</strong></p> <p> </p> <p>Automated image-based cervical cancer detection plays a vital role in diagnosing cervical cancer, particularly through the use of digital cervical images obtained via visual inspection with acetic acid (VIA). Many algorithms have been developed to classify these images by extracting mathematical features. Artificial intelligence (AI) has significantly advanced healthcare by improving disease detection, diagnosis, and prediction of health outcomes. While various cervical cancer screening methods have evolved, VIA remains one of the most feasible options in low-resource settings. However, its effectiveness relies heavily on the examiner’s experience, which can be limited due to a shortage of qualified healthcare professionals. This study evaluates the performance of AI image processing techniques for detecting cervical cancer using VIA images. The research compares four traditional machine learning techniques and six deep learning techniques in classifying cervical cancer images, where each model was trained on four randomly selected batches of images (300, 700, 1000, and 1678 images) to assess model performance with an increasing number of training images. The VGG19 model achieved a consistent accuracy of 81% across all training sizes. The Vision Transformer (ViT) model, on the other hand, showed a performance improvement from 57% accuracy with 300 images to 77% accuracy with 1678 images. The hybrid model, combining VGG19 and ViT, demonstrated superior performance with an accuracy of 86.65%, an AUC of 0.85, a sensitivity of 0.832, and a specificity of 0.8485. This study demonstrates that the hybrid model outperforms individual models, offering a promising solution for cervical cancer detection in low-resource environments.</p>Fatima Binta AdamuMuhammad Bashir AbdullahiSulaimon Adebayo BashirAbiodun Musa Aibinu
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2024-01-282024-01-28122