Pub Date : 2024-08-10DOI: 10.46481/jnsps.2024.2025
Josephine Elamma Ochigbo, J. Ndam, Wipuni U. Sirisena
Malaria remains a global threat and the conventional methods used for combating the disease leave out mosquitoes that feed outdoors and this study addresses the challenge posed by such mosquitoes based on a tool called ivermectin drug which is lethal to mosquitoes that ingest bloodmeal containing a concentration of it. We formulated a mathematical model with three control tools (insecticide treated nets, treatment of infective individuals and ivermectin drug on livestock and human) for the transmission and control of malaria under optimal condition. The model’s basic reproduction number, R0 was estimated and the local and global stability analyses of the disease-free and endemic equilibrium points of the model were analyzed. Sensitivity analysis carried out showed that R0 is most sensitive to the mosquito biting rate and to the proportion of blood meal on human with cattle availability in such a way that any percent increase in the value of any of these parameters will lead to an equal percent increase in the value of R0. The result of an optimal control analysis based on three time dependent controls suggests that the combination of all three controls gives the best result followed by the strategy that combines the use of ivermectin drug and the treatment of infective human. Depending on available resources, any of these is recommended to be adopted in malaria intervention programmes because of their effectiveness on both the infective human and mosquito populations with the potential of contributing significantly to the disease elimination within a minimal time frame.
{"title":"Optimal control with the effects of ivermectin and livestock availability on malaria transmission","authors":"Josephine Elamma Ochigbo, J. Ndam, Wipuni U. Sirisena","doi":"10.46481/jnsps.2024.2025","DOIUrl":"https://doi.org/10.46481/jnsps.2024.2025","url":null,"abstract":"Malaria remains a global threat and the conventional methods used for combating the disease leave out mosquitoes that feed outdoors and this study addresses the challenge posed by such mosquitoes based on a tool called ivermectin drug which is lethal to mosquitoes that ingest bloodmeal containing a concentration of it. We formulated a mathematical model with three control tools (insecticide treated nets, treatment of infective individuals and ivermectin drug on livestock and human) for the transmission and control of malaria under optimal condition. The model’s basic reproduction number, R0 was estimated and the local and global stability analyses of the disease-free and endemic equilibrium points of the model were analyzed. Sensitivity analysis carried out showed that R0 is most sensitive to the mosquito biting rate and to the proportion of blood meal on human with cattle availability in such a way that any percent increase in the value of any of these parameters will lead to an equal percent increase in the value of R0. The result of an optimal control analysis based on three time dependent controls suggests that the combination of all three controls gives the best result followed by the strategy that combines the use of ivermectin drug and the treatment of infective human. Depending on available resources, any of these is recommended to be adopted in malaria intervention programmes because of their effectiveness on both the infective human and mosquito populations with the potential of contributing significantly to the disease elimination within a minimal time frame.","PeriodicalId":342917,"journal":{"name":"Journal of the Nigerian Society of Physical Sciences","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141920532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-10DOI: 10.46481/jnsps.2024.2198
Lek Ming Lim, Lu Yang, Ahmad Sufril Azlan Mohamed, Majid Khan Majahar Ali
Pulau Pinang has introduced several measures to enhance traffic safety and promote sustainability, including the installation of CCTV systems and the implementation of smart solutions and green technology as part of the Penang 2030 vision, aligning with the Sustainable Development Goals (SDGs). However, despite these efforts, road accidents persist due to non-optimised detection models, incomplete data from manual reporting, and technological constraints in real-time video analysis and predictive modelling. This study evaluates the effectiveness of the YOLOv7+G3HN framework for vehicle detection and near-miss analysis, with a focus on the influence of video quality on detection performance. The research aims to understand how high- and low-quality video inputs affect the accuracy and computational efficiency of detection algorithms. High[1]quality videos resulted in significantly faster computation times for vehicle detection than low-quality videos, highlighting the importance of video resolution in optimising detection processes. Despite the robustness of the algorithm, with no errors detected in both video qualities, higher miss detection rates in low-quality videos suggest that lower resolution may compromise detection accuracy and the effectiveness of monitoring systems. Near-miss analysis revealed that high-quality videos had a lower probability of near-miss occurrences than low-quality videos, highlighting the importance of video resolution for detection efficacy. These findings emphasise the critical role of high-resolution video inputs in enhancing detection accuracy and reliability, advocating for their implementation to optimise vehicle detection and improve road safety. Additionally, YOLOv7+G3HN outperforms YOLOv7 in both accuracy and speed. The study concludes that the YOLOv7+G3HN framework is effective for vehicle detection and near-miss analysis, provided that video quality is considered in system design and implementation.
{"title":"Data safety prediction using YOLOv7+G3HN for traffic roads","authors":"Lek Ming Lim, Lu Yang, Ahmad Sufril Azlan Mohamed, Majid Khan Majahar Ali","doi":"10.46481/jnsps.2024.2198","DOIUrl":"https://doi.org/10.46481/jnsps.2024.2198","url":null,"abstract":"Pulau Pinang has introduced several measures to enhance traffic safety and promote sustainability, including the installation of CCTV systems and the implementation of smart solutions and green technology as part of the Penang 2030 vision, aligning with the Sustainable Development Goals (SDGs). However, despite these efforts, road accidents persist due to non-optimised detection models, incomplete data from manual reporting, and technological constraints in real-time video analysis and predictive modelling. This study evaluates the effectiveness of the YOLOv7+G3HN framework for vehicle detection and near-miss analysis, with a focus on the influence of video quality on detection performance. The research aims to understand how high- and low-quality video inputs affect the accuracy and computational efficiency of detection algorithms. High[1]quality videos resulted in significantly faster computation times for vehicle detection than low-quality videos, highlighting the importance of video resolution in optimising detection processes. Despite the robustness of the algorithm, with no errors detected in both video qualities, higher miss detection rates in low-quality videos suggest that lower resolution may compromise detection accuracy and the effectiveness of monitoring systems. Near-miss analysis revealed that high-quality videos had a lower probability of near-miss occurrences than low-quality videos, highlighting the importance of video resolution for detection efficacy. These findings emphasise the critical role of high-resolution video inputs in enhancing detection accuracy and reliability, advocating for their implementation to optimise vehicle detection and improve road safety. Additionally, YOLOv7+G3HN outperforms YOLOv7 in both accuracy and speed. The study concludes that the YOLOv7+G3HN framework is effective for vehicle detection and near-miss analysis, provided that video quality is considered in system design and implementation.\u0000 ","PeriodicalId":342917,"journal":{"name":"Journal of the Nigerian Society of Physical Sciences","volume":"21 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141920704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-27DOI: 10.46481/jnsps.2024.2039
Akila Dabara Kayit, Mohd Ismail Tahir
The research introduces a method for anticipating stock market patterns by combining machine learning techniques with analysis methods. Multiple machine learning algorithms were integrated to address the limitations of stock market forecasting models. Using web scraping techniques, data were gathered from the S&P500 index over seven years, from September 5, 2016, to August 5, 2023. Companies like Microsoft Corporation (MSFT), Amazon.com Inc. (AMZN), JPMorgan Chase & Co (JPM), and Tesla, Inc. (TSLA) were selected based on their inclusion in the S&P 500 index. LR, RF, SVC, ADAB, and XGBC algorithms were applied as models by utilising optimisation using grid search and single algorithm approaches. Voting methods were employed to combine predictions from these models. The study employed rigorous statistical analyses, including the Kruskal-Wallis test to assess overall differences, followed by Pairwise Dunn’s Test with Bonferroni Correction for detailed algorithm comparisons. Additionally, Bootstrapping was utilised to calculate Confidence Intervals (CI) for robust estimation of algorithm performance. The methodology covered data collection, preprocessing, model training, and performance assessment. The outcomes indicate that the proposed approach accurately forecasts stock trends precisely and dependably. This study contributes to refining stock market prediction methodologies by introducing a strategy that enhances prediction accuracy while offering investors and financial professionals insights. Furthermore, assessing algorithm performance across metrics and companies highlights the versatility and effectiveness of machine-learning approaches in the fields.
{"title":"Novel way to predict stock movements using multiple models and comprehensive analysis: leveraging voting meta-ensemble techniques","authors":"Akila Dabara Kayit, Mohd Ismail Tahir","doi":"10.46481/jnsps.2024.2039","DOIUrl":"https://doi.org/10.46481/jnsps.2024.2039","url":null,"abstract":"The research introduces a method for anticipating stock market patterns by combining machine learning techniques with analysis methods. Multiple machine learning algorithms were integrated to address the limitations of stock market forecasting models. Using web scraping techniques, data were gathered from the S&P500 index over seven years, from September 5, 2016, to August 5, 2023. Companies like Microsoft Corporation (MSFT), Amazon.com Inc. (AMZN), JPMorgan Chase & Co (JPM), and Tesla, Inc. (TSLA) were selected based on their inclusion in the S&P 500 index. LR, RF, SVC, ADAB, and XGBC algorithms were applied as models by utilising optimisation using grid search and single algorithm approaches. Voting methods were employed to combine predictions from these models. The study employed rigorous statistical analyses, including the Kruskal-Wallis test to assess overall differences, followed by Pairwise Dunn’s Test with Bonferroni Correction for detailed algorithm comparisons. Additionally, Bootstrapping was utilised to calculate Confidence Intervals (CI) for robust estimation of algorithm performance. The methodology covered data collection, preprocessing, model training, and performance assessment. The outcomes indicate that the proposed approach accurately forecasts stock trends precisely and dependably. This study contributes to refining stock market prediction methodologies by introducing a strategy that enhances prediction accuracy while offering investors and financial professionals insights. Furthermore, assessing algorithm performance across metrics and companies highlights the versatility and effectiveness of machine-learning approaches in the fields.","PeriodicalId":342917,"journal":{"name":"Journal of the Nigerian Society of Physical Sciences","volume":"4 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141797611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-27DOI: 10.46481/jnsps.2024.2093
Gabriel James, Ekong Anietie, Etimbuk Abraham, Enobong Oduobuk, Peace Okafor
This study proposed a machine learning approach to predict the scalability of broadband networks, which is crucial for ensuring fast and reliable internet connectivity. Scalability measures a network’s ability to handle increasing users, devices, and data traffic without compromising performance. The researchers leveraged the strengths of Random Forest (RF) and Support Vector Machine (SVM) algorithms to predict scalability. A large dataset of 40,000 data points was collected, focusing on six key metrics: Response Time, Bandwidth, Latency, Error Rate, Throughput, and Number of Users Connected. The data was preprocessed and divided into training and testing sets (80:20 ratio). Both RF and SVM algorithms were trained on the dataset, and a comparative analysis was conducted to determine which algorithm performed better. The results showed that the RF model outperformed the SVM model, achieving an accuracy of 95.0% compared to 91.0%. The RF model also exhibited higher precision, recall, and AUC scores. Feature importance analysis revealed that Response Time and Throughput were the most significant factors in determining network scalability. The study demonstrated the effectiveness of the RF model in predicting broadband network scalability, with a lower loss value (0.0133 for training and 0.0160 for validation) compared to the SVM model. This approach will help network operators and administrators predict and improve network scalability, ensuring reliable and fast internet connectivity. The study contributes to the development of machine learning-based solutions for broadband network performance evaluation and optimization.
{"title":"Analysis of support vector machine and random forest models for predicting the scalability of a broadband network","authors":"Gabriel James, Ekong Anietie, Etimbuk Abraham, Enobong Oduobuk, Peace Okafor","doi":"10.46481/jnsps.2024.2093","DOIUrl":"https://doi.org/10.46481/jnsps.2024.2093","url":null,"abstract":"This study proposed a machine learning approach to predict the scalability of broadband networks, which is crucial for ensuring fast and reliable internet connectivity. Scalability measures a network’s ability to handle increasing users, devices, and data traffic without compromising performance. The researchers leveraged the strengths of Random Forest (RF) and Support Vector Machine (SVM) algorithms to predict scalability. A large dataset of 40,000 data points was collected, focusing on six key metrics: Response Time, Bandwidth, Latency, Error Rate, Throughput, and Number of Users Connected. The data was preprocessed and divided into training and testing sets (80:20 ratio). Both RF and SVM algorithms were trained on the dataset, and a comparative analysis was conducted to determine which algorithm performed better. The results showed that the RF model outperformed the SVM model, achieving an accuracy of 95.0% compared to 91.0%. The RF model also exhibited higher precision, recall, and AUC scores. Feature importance analysis revealed that Response Time and Throughput were the most significant factors in determining network scalability. The study demonstrated the effectiveness of the RF model in predicting broadband network scalability, with a lower loss value (0.0133 for training and 0.0160 for validation) compared to the SVM model. This approach will help network operators and administrators predict and improve network scalability, ensuring reliable and fast internet connectivity. The study contributes to the development of machine learning-based solutions for broadband network performance evaluation and optimization.","PeriodicalId":342917,"journal":{"name":"Journal of the Nigerian Society of Physical Sciences","volume":"91 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141797814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-27DOI: 10.46481/jnsps.2024.2137
L. G. Prasad, R. Ravikumar, R. G. Raman, R. R. Kannad
CuO, MnO and CuMnO nanomaterials have been prepared using chemical precipitation method. Structure of the nanomaterials have been confirmed using XRD analysis. Metal oxide vibration have been identified and assigned to the vibrational band. Morophology of the prepared nanomaterials have been investigated in the SEM images. Optical band of the materials have been calculated using Tauc’s relation from the UV-Vis spectrum. Photocatalytic nature of the prepared nanomaterials has been studied against Congo red and Malachite green dyes.
{"title":"Investigations on the structural, vibrational, optical and photocatalytic behavior of CuO, MnO and CuMnO nanomaterials","authors":"L. G. Prasad, R. Ravikumar, R. G. Raman, R. R. Kannad","doi":"10.46481/jnsps.2024.2137","DOIUrl":"https://doi.org/10.46481/jnsps.2024.2137","url":null,"abstract":"CuO, MnO and CuMnO nanomaterials have been prepared using chemical precipitation method. Structure of the nanomaterials have been confirmed using XRD analysis. Metal oxide vibration have been identified and assigned to the vibrational band. Morophology of the prepared nanomaterials have been investigated in the SEM images. Optical band of the materials have been calculated using Tauc’s relation from the UV-Vis spectrum. Photocatalytic nature of the prepared nanomaterials has been studied against Congo red and Malachite green dyes.","PeriodicalId":342917,"journal":{"name":"Journal of the Nigerian Society of Physical Sciences","volume":"77 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141798240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-21DOI: 10.46481/jnsps.2024.2128
K. Aladeniyi
In a study of 56 randomly selected Traditional Mud Houses (TMHs) in Nigeria, the radiation health impacts of radon progeny were evaluated using CR-39 radon detectors within a rainy season. The measured radon concentration ranged from 17 to 174 Bq m-3, with an average value of 76 Bq m-3 (SD = 36). This was lower than the WHO’s recommended reference level of 100 Bq m-3. However, 24% of the surveyed houses exceeded this level, indicating potential health risks. The estimated Potential Alpha Energy Concentration (PAEC) due to its progeny ranged from 1.84 to 18.81 mWL with an average value of 8.24 mWL (SD=3.91). The computed annual effective doses yielded an average value of 3.06 ± 1.44 mSv y1, which is far less than the recommended reference level of 10 mSv y1 by the International Commission on Radiological Protection. The lifetime excess absolute risks varied from 0.4 × 104 to 3.9 × 104, with an average of 1.7 ± 0.8 × 104. Improved the ventilation systems, the application of cements plaster and distempers to the building walls and floor were recommended for older TMHs with ages greater than 50 y to mitigate radon exposure. This data can inform potential policy measures for indoor radon progeny control in Nigeria.
{"title":"Health risk evaluation of radon progeny exposure in Nigerian traditional mud houses","authors":"K. Aladeniyi","doi":"10.46481/jnsps.2024.2128","DOIUrl":"https://doi.org/10.46481/jnsps.2024.2128","url":null,"abstract":"In a study of 56 randomly selected Traditional Mud Houses (TMHs) in Nigeria, the radiation health impacts of radon progeny were evaluated using CR-39 radon detectors within a rainy season. The measured radon concentration ranged from 17 to 174 Bq m-3, with an average value of 76 Bq m-3 (SD = 36). This was lower than the WHO’s recommended reference level of 100 Bq m-3. However, 24% of the surveyed houses exceeded this level, indicating potential health risks. The estimated Potential Alpha Energy Concentration (PAEC) due to its progeny ranged from 1.84 to 18.81 mWL with an average value of 8.24 mWL (SD=3.91). The computed annual effective doses yielded an average value of 3.06 ± 1.44 mSv y1, which is far less than the recommended reference level of 10 mSv y1 by the International Commission on Radiological Protection. The lifetime excess absolute risks varied from 0.4 × 104 to 3.9 × 104, with an average of 1.7 ± 0.8 × 104. Improved the ventilation systems, the application of cements plaster and distempers to the building walls and floor were recommended for older TMHs with ages greater than 50 y to mitigate radon exposure. This data can inform potential policy measures for indoor radon progeny control in Nigeria.","PeriodicalId":342917,"journal":{"name":"Journal of the Nigerian Society of Physical Sciences","volume":"58 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141817917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-21DOI: 10.46481/jnsps.2024.2103
S. Oladipo, Adesola A. Adeleke, A. A. Badeji, Katherine I. Babalola, A. H. Labulo, Ibrahim Hassan, Sadiq T. Yussuf, Samuel O. Olalekan
Two Schiff bases, (E)-N-(4-bromophenyl)-1-(2,6-dichlorophenyl)methanimine (S1) and (E)-4-(((4-bromophenyl)imino)methyl)phenol (S2) were synthesized and elucidated using basic spectroscopic methods such as Fourier-transform-infrared, Ultraviolet-visible as well as 1H & 13C-NMR while purity of the compounds was ascertained by the means of elemental analysis. The stability, chemical reactivities and electronic properties of S1 and S2 were evaluated using Density Functional Theory (DFT). Energy band gap (?E) of S1 and S2 are 2.81 eV and 3.02 eV respectively and this inferred that S1 is more reactive as well as less stable than S2. The Molecular electrostatic potential (MEP) mapping evaluation showed that both S1 and S2 are having more of the yellow, green and red region than the blue region, signifying that these structures are prone to electrophilic attack. The compounds were screened against one Gram-negative (Staphylococcus aureus) and two Gram-positive bacteria (Klebsiella pneumoniae and Psedomonas aeruginosa) to evaluate their antibacterial potential. S1 as well as S2 showed antibacterial potential against Staphylococcus aureus and exhibited less of no activity against Klebsiella pneumoniae and Escherichia coli. In silico studies of S1 and S2 against Staphylococcus aureus was carried out and the outcomes corroborate with experimental findings. Pharmacological evaluation of S1 and S2 showed that both compounds exhibited less violation of Lipinski’s rule of five (Ro5) which pose them to be less toxic and orally available.
{"title":"Computational investigation and biological activity of selected Schiff bases","authors":"S. Oladipo, Adesola A. Adeleke, A. A. Badeji, Katherine I. Babalola, A. H. Labulo, Ibrahim Hassan, Sadiq T. Yussuf, Samuel O. Olalekan","doi":"10.46481/jnsps.2024.2103","DOIUrl":"https://doi.org/10.46481/jnsps.2024.2103","url":null,"abstract":"Two Schiff bases, (E)-N-(4-bromophenyl)-1-(2,6-dichlorophenyl)methanimine (S1) and (E)-4-(((4-bromophenyl)imino)methyl)phenol (S2) were synthesized and elucidated using basic spectroscopic methods such as Fourier-transform-infrared, Ultraviolet-visible as well as 1H & 13C-NMR while purity of the compounds was ascertained by the means of elemental analysis. The stability, chemical reactivities and electronic properties of S1 and S2 were evaluated using Density Functional Theory (DFT). Energy band gap (?E) of S1 and S2 are 2.81 eV and 3.02 eV respectively and this inferred that S1 is more reactive as well as less stable than S2. The Molecular electrostatic potential (MEP) mapping evaluation showed that both S1 and S2 are having more of the yellow, green and red region than the blue region, signifying that these structures are prone to electrophilic attack. The compounds were screened against one Gram-negative (Staphylococcus aureus) and two Gram-positive bacteria (Klebsiella pneumoniae and Psedomonas aeruginosa) to evaluate their antibacterial potential. S1 as well as S2 showed antibacterial potential against Staphylococcus aureus and exhibited less of no activity against Klebsiella pneumoniae and Escherichia coli. In silico studies of S1 and S2 against Staphylococcus aureus was carried out and the outcomes corroborate with experimental findings. Pharmacological evaluation of S1 and S2 showed that both compounds exhibited less violation of Lipinski’s rule of five (Ro5) which pose them to be less toxic and orally available.","PeriodicalId":342917,"journal":{"name":"Journal of the Nigerian Society of Physical Sciences","volume":"43 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141818031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-14DOI: 10.46481/jnsps.2024.2113
Enobong R. Essien, V. N. Atasie, Ngozi A. Adeleye, L. Adams
Structural alteration of synthetic implants aims to achieve better bioactivity, higher cellular response, and regulated degradability, all of which are critical criteria for a biomaterial to serve as a graft in bone regeneration. The aim of this work was to synthesize silicon-substituted hydroxyapatite and test its bioactivity in simulated body fluid (SBF) by proving the use of sodium metasilicate (Na2SiO3.9H2O) as an affordable precursor of silica. Thus, the study evaluated the in vitro bone-bonding capacity of hydroxyapatite (Ca10(PO4)6(OH)2) (HA) substituted with silicate ion (Ca10(PO4)6-x(SiO4)x(OH)2-x; SixHA). The SixHA with x = 0.4 was synthesized by utilizing a wet precipitation method with sodium metasilicate as a low-cost silica alternative for alkoxysilane precursors. The SixHA was then examined for properties such as morphology, elemental composition, phase composition, and the nature of chemical bonds using scanning electron microscopy (SEM), energy dispersive X-ray analysis (EDX), X-ray diffractometry (XRD), and Fourier transformed infrared spectroscopy (FTIR), respectively. An in vitro bioactivity experiment was also carried out by incubating the SixHA in simulated body fluid (SBF) at 36.5 °C for 7 and 14 days. The obtained results revealed the substitution of SiO44- for some PO43- groups in the hydroxyapatite structure. The SixHA nucleated more apatite crystals on its surface and demonstrated some degradability during the periods of immersion in SBF. The characteristics of the SixHA imply that it could be used as a graft in bone restoration applications, thus signifying that sodium metasilicate could serve as an economic silica source for silicon-substituted hydroxyapatite production.
{"title":"Synthesis and in vitro bioactivity of sodium metasilicate-derived silicon-substituted hydroxyapatite","authors":"Enobong R. Essien, V. N. Atasie, Ngozi A. Adeleye, L. Adams","doi":"10.46481/jnsps.2024.2113","DOIUrl":"https://doi.org/10.46481/jnsps.2024.2113","url":null,"abstract":"Structural alteration of synthetic implants aims to achieve better bioactivity, higher cellular response, and regulated degradability, all of which are critical criteria for a biomaterial to serve as a graft in bone regeneration. The aim of this work was to synthesize silicon-substituted hydroxyapatite and test its bioactivity in simulated body fluid (SBF) by proving the use of sodium metasilicate (Na2SiO3.9H2O) as an affordable precursor of silica. Thus, the study evaluated the in vitro bone-bonding capacity of hydroxyapatite (Ca10(PO4)6(OH)2) (HA) substituted with silicate ion (Ca10(PO4)6-x(SiO4)x(OH)2-x; SixHA). The SixHA with x = 0.4 was synthesized by utilizing a wet precipitation method with sodium metasilicate as a low-cost silica alternative for alkoxysilane precursors. The SixHA was then examined for properties such as morphology, elemental composition, phase composition, and the nature of chemical bonds using scanning electron microscopy (SEM), energy dispersive X-ray analysis (EDX), X-ray diffractometry (XRD), and Fourier transformed infrared spectroscopy (FTIR), respectively. An in vitro bioactivity experiment was also carried out by incubating the SixHA in simulated body fluid (SBF) at 36.5 °C for 7 and 14 days. The obtained results revealed the substitution of SiO44- for some PO43- groups in the hydroxyapatite structure. The SixHA nucleated more apatite crystals on its surface and demonstrated some degradability during the periods of immersion in SBF. The characteristics of the SixHA imply that it could be used as a graft in bone restoration applications, thus signifying that sodium metasilicate could serve as an economic silica source for silicon-substituted hydroxyapatite production. ","PeriodicalId":342917,"journal":{"name":"Journal of the Nigerian Society of Physical Sciences","volume":"56 42","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141649742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-14DOI: 10.46481/jnsps.2024.2102
Gabriel James, Ime Jonah Umoren, Saviour Inyang, Oscar Aloysius
This study addresses the growing concern of technostress, a condition caused by the overwhelming use of digital technologies, exacerbated by the COVID-19 pandemic. The researchers developed a predictive model using machine learning algorithms, Random Forest (RF) and Support Vector Machine (SVM), to assess and manage technostress levels. The model considers factors such as age, gender, technology usage hours, and technological experiences to classify stress levels into high, moderate, and low categories. The study collected data through a questionnaire administered to knowledgeable respondents, using a non-probabilistic sampling approach. The results showed that both RF and SVM algorithms achieved high accuracy in classifying technostress, with SVM performing slightly better (94.5% vs 84.50%). The model’s effectiveness in predicting stress levels for users with varying degrees of stress is a significant contribution to the field. The research also developed an interactive user interface to facilitate user engagement with the model, promoting stress management and well-being in a technology-driven society. The study’s findings provide valuable insights into the challenges posed by technostress and offer a solution for mitigating its effects. The use of machine learning algorithms to classify gender based on the dataset demonstrates the model’s potential applications in various areas. Overall, this study demonstrates the importance of addressing technostress in the digital age and provides a valuable tool for managing stress levels. The development of predictive models like this one can help individuals and organizations mitigate the negative impacts of technostress, promoting a healthier and more sustainable relationship with technology.
{"title":"Analysis of support vector machine and random forest models for classification of the impact of technostress in covid and post-covid era","authors":"Gabriel James, Ime Jonah Umoren, Saviour Inyang, Oscar Aloysius","doi":"10.46481/jnsps.2024.2102","DOIUrl":"https://doi.org/10.46481/jnsps.2024.2102","url":null,"abstract":"This study addresses the growing concern of technostress, a condition caused by the overwhelming use of digital technologies, exacerbated by the COVID-19 pandemic. The researchers developed a predictive model using machine learning algorithms, Random Forest (RF) and Support Vector Machine (SVM), to assess and manage technostress levels. The model considers factors such as age, gender, technology usage hours, and technological experiences to classify stress levels into high, moderate, and low categories. The study collected data through a questionnaire administered to knowledgeable respondents, using a non-probabilistic sampling approach. The results showed that both RF and SVM algorithms achieved high accuracy in classifying technostress, with SVM performing slightly better (94.5% vs 84.50%). The model’s effectiveness in predicting stress levels for users with varying degrees of stress is a significant contribution to the field. The research also developed an interactive user interface to facilitate user engagement with the model, promoting stress management and well-being in a technology-driven society. The study’s findings provide valuable insights into the challenges posed by technostress and offer a solution for mitigating its effects. The use of machine learning algorithms to classify gender based on the dataset demonstrates the model’s potential applications in various areas. Overall, this study demonstrates the importance of addressing technostress in the digital age and provides a valuable tool for managing stress levels. The development of predictive models like this one can help individuals and organizations mitigate the negative impacts of technostress, promoting a healthier and more sustainable relationship with technology.","PeriodicalId":342917,"journal":{"name":"Journal of the Nigerian Society of Physical Sciences","volume":"59 42","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141649507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-07DOI: 10.46481/jnsps.2024.1995
B. Bako, E. Etim, J. P. Shinggu, S. S. Humphrey, L. J. Moses, M. E. Khan
The discovery of lupeol, a triterpenoid compound (C30H50O), in the ethyl acetate leaf extract of Justicia secunda (Blood root), has opened doors to extensive research and development opportunities in natural product-based pharmaceuticals. Lupeol’s versatile pharmacological properties, including anti-inflammatory, anticancer, antidiabetic, and antiviral effects, make it a compelling candidate for drug development. To fully harness its potential, a comprehensive understanding of lupeol’s structural and chemical attributes is crucial. Through quantum chemical calculations using the GAUSSIAN 09 suite of programs, we determined the optimized geometry, IR frequencies, bond distances (R), bond angles (A), dipole moments, HOMO-LUMO and other molecular parameters for this solitary molecule. The remarkable accuracy and reliability of computational techniques in predicting the properties of systems and reactants are evident in the consistently favorable results. A strong concordance and consistency between the experimental and computational outcomes further reinforces the credibility of our findings. This study offers a means to explore lupeol’s molecular behavior, providing insights that can guide future drug development efforts rooted in this promising natural compound.
{"title":"Quantum chemical calculations of lupeol (C30H50O) isolated from the ethyl acetate leaf extracts of Justicia Secunda","authors":"B. Bako, E. Etim, J. P. Shinggu, S. S. Humphrey, L. J. Moses, M. E. Khan","doi":"10.46481/jnsps.2024.1995","DOIUrl":"https://doi.org/10.46481/jnsps.2024.1995","url":null,"abstract":"The discovery of lupeol, a triterpenoid compound (C30H50O), in the ethyl acetate leaf extract of Justicia secunda (Blood root), has opened doors to extensive research and development opportunities in natural product-based pharmaceuticals. Lupeol’s versatile pharmacological properties, including anti-inflammatory, anticancer, antidiabetic, and antiviral effects, make it a compelling candidate for drug development. To fully harness its potential, a comprehensive understanding of lupeol’s structural and chemical attributes is crucial. Through quantum chemical calculations using the GAUSSIAN 09 suite of programs, we determined the optimized geometry, IR frequencies, bond distances (R), bond angles (A), dipole moments, HOMO-LUMO and other molecular parameters for this solitary molecule. The remarkable accuracy and reliability of computational techniques in predicting the properties of systems and reactants are evident in the consistently favorable results. A strong concordance and consistency between the experimental and computational outcomes further reinforces the credibility of our findings. This study offers a means to explore lupeol’s molecular behavior, providing insights that can guide future drug development efforts rooted in this promising natural compound.","PeriodicalId":342917,"journal":{"name":"Journal of the Nigerian Society of Physical Sciences","volume":" 27","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141670926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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