Pub Date : 2024-08-10DOI: 10.9734/jerr/2024/v26i81248
Andikan E. Okoro, E. Ubom, Ubong Ukommi
Effective network anomaly detection plays a pivotal role in safeguarding digital assets against evolving cyber threats in cybersecurity. In this study, the NSL-KDD dataset was used to investigate anomaly detection using support Vector Machines (SVM) with various kernels: linear, polynomial, radial basis function (RBF), and sigmoid. The linear kernel SVM achieved a high accuracy of 99.47% and an F-score of 99.47%. Despite its strong overall performance, indicated by a weighted average F-score of 0.99, the macro average F-score of 0.79 suggested variability in class performance. Several classes, such as 0, 11, 12, 13, and 20, achieved perfect precision and recall, while classes 1, 7, 8, 16, and 19 had zero recall and F-scores. The Polynomial Kernel SVM demonstrated an accuracy of 99.55% and an F-score of 99.53%. It also showed high precision and recall for many classes, achieving a weighted average F-score of 1.00. However, the macro average F-score of 0.72 indicated notable variation, with poor performance in classes 1, 7, 8, 16, 19, and 22. The RBF Kernel SVM also recorded an accuracy of 99.55% and an F-score of 99.53%, with a macro and weighted average of 0.48 and 0.92 respectively. While several classes achieved perfect scores, significant performance drops were observed in classes 1, 7, 8, 16, 19, and 22. The Sigmoid Kernel SVM showed a lower overall effectiveness with an accuracy of 92.11% and an F-score of 91.80%. The macro and the weighted average of 0.79 and 0.99 respectively and exhibited considerable inconsistency, with some classes achieving high precision and recall while 1, 8, 12, 13, 16, 19, and 22, performed poorly. While the Linear and Poly Kernels showed strong overall performance, the RBF and Sigmoid Kernels exhibited greater variability across different classes, with the Sigmoid Kernel being the least effective for anomaly detection in this dataset.
{"title":"Evaluation of Machine Learning Model for Network Anomaly Detection: Support Vector Machine","authors":"Andikan E. Okoro, E. Ubom, Ubong Ukommi","doi":"10.9734/jerr/2024/v26i81248","DOIUrl":"https://doi.org/10.9734/jerr/2024/v26i81248","url":null,"abstract":"Effective network anomaly detection plays a pivotal role in safeguarding digital assets against evolving cyber threats in cybersecurity. In this study, the NSL-KDD dataset was used to investigate anomaly detection using support Vector Machines (SVM) with various kernels: linear, polynomial, radial basis function (RBF), and sigmoid. The linear kernel SVM achieved a high accuracy of 99.47% and an F-score of 99.47%. Despite its strong overall performance, indicated by a weighted average F-score of 0.99, the macro average F-score of 0.79 suggested variability in class performance. Several classes, such as 0, 11, 12, 13, and 20, achieved perfect precision and recall, while classes 1, 7, 8, 16, and 19 had zero recall and F-scores. The Polynomial Kernel SVM demonstrated an accuracy of 99.55% and an F-score of 99.53%. It also showed high precision and recall for many classes, achieving a weighted average F-score of 1.00. However, the macro average F-score of 0.72 indicated notable variation, with poor performance in classes 1, 7, 8, 16, 19, and 22. The RBF Kernel SVM also recorded an accuracy of 99.55% and an F-score of 99.53%, with a macro and weighted average of 0.48 and 0.92 respectively. While several classes achieved perfect scores, significant performance drops were observed in classes 1, 7, 8, 16, 19, and 22. The Sigmoid Kernel SVM showed a lower overall effectiveness with an accuracy of 92.11% and an F-score of 91.80%. The macro and the weighted average of 0.79 and 0.99 respectively and exhibited considerable inconsistency, with some classes achieving high precision and recall while 1, 8, 12, 13, 16, 19, and 22, performed poorly. While the Linear and Poly Kernels showed strong overall performance, the RBF and Sigmoid Kernels exhibited greater variability across different classes, with the Sigmoid Kernel being the least effective for anomaly detection in this dataset.","PeriodicalId":340494,"journal":{"name":"Journal of Engineering Research and Reports","volume":"5 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141920771","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 : 2024-08-09DOI: 10.9734/jerr/2024/v26i81247
Alih John Eko
According to some reports, yttria-stabilized zirconia has remained one of the most applied TBC materials due to its excellent thermal insulation properties and very high melting point, besides high resistance to thermal shock. In this paper, an overview of the most recent developments on YSZ-based TBCs is given, basically on the improvement of their thermal stability, sintering resistance, and phase stability. It further reviews very recent advances in fabrication techniques and deposition techniques and nanostructuring, which have been extremely useful in improving the performance and durability of YSZ coatings. Furthermore, in the review, the incorporation of new, novel dopants or alternative ceramic materials is given for further optimization of properties for TBCs. This paper finally concludes by giving the current challenges and future directions of YSZ-based TBC development, with the note that more R&D efforts need to be directed towards it, more so in view of the growing demands in the high-temperature applications domain for gas turbines and aero engines.
{"title":"A Short Review of Yttria-Stabilized Zirconia (YSZ) for Thermal Barrier Coatings: Recent Progress","authors":"Alih John Eko","doi":"10.9734/jerr/2024/v26i81247","DOIUrl":"https://doi.org/10.9734/jerr/2024/v26i81247","url":null,"abstract":"According to some reports, yttria-stabilized zirconia has remained one of the most applied TBC materials due to its excellent thermal insulation properties and very high melting point, besides high resistance to thermal shock. In this paper, an overview of the most recent developments on YSZ-based TBCs is given, basically on the improvement of their thermal stability, sintering resistance, and phase stability. It further reviews very recent advances in fabrication techniques and deposition techniques and nanostructuring, which have been extremely useful in improving the performance and durability of YSZ coatings. Furthermore, in the review, the incorporation of new, novel dopants or alternative ceramic materials is given for further optimization of properties for TBCs. This paper finally concludes by giving the current challenges and future directions of YSZ-based TBC development, with the note that more R&D efforts need to be directed towards it, more so in view of the growing demands in the high-temperature applications domain for gas turbines and aero engines.","PeriodicalId":340494,"journal":{"name":"Journal of Engineering Research and Reports","volume":"6 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141921815","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}
Hydrothermal method is used to prepare activated carbon (AC) supported chromium-based catalysts for the catalytic combustion of 1,2-dichloroethane (DCE). The physicochemical properties relating catalysts are evaluated by several characterization techniques such as XRD, SEM, XPS, N2 adsorption-desorption isotherms, H2-TPR and NH3-TPR. The outcomes demonstrated that the Cr2O3 is the perfect targeted base catalyst adding to the facts that the hydrothermal approach is an appropriate way to create catalysts with superior properties. The textural characteristics, reducibility, and acidity of the Cr-based catalysts were all enhanced by the addition of cerium (Ce) and AC. Nevertheless, the research shows how crucial the Ce doping amount is for adjusting the physicochemical characteristics and catalytic efficacy of Cr-based catalysts. The catalyst 20Ce-Cr/AC has the maximum catalytic activity when it came to DCE combustion, where its T50 and T90 attaining of 247°C and 269.5°C, correspondingly. This is explained by the ideal Ce doping level, which improves the redox characteristics and revealed more active sites.
{"title":"The Affects of Ce Doping Cr(_2)O(_3) Based Catalysts Supported on Activated Carbon for 1,2-Dichloroethane Abatement","authors":"Tabet Tsiankoraka Ahmadaly Adam, Pengfei Zhu, Na Li, Zhaoxia Hu, Shouwen Chen","doi":"10.9734/jerr/2024/v26i61169","DOIUrl":"https://doi.org/10.9734/jerr/2024/v26i61169","url":null,"abstract":"Hydrothermal method is used to prepare activated carbon (AC) supported chromium-based catalysts for the catalytic combustion of 1,2-dichloroethane (DCE). The physicochemical properties relating catalysts are evaluated by several characterization techniques such as XRD, SEM, XPS, N2 adsorption-desorption isotherms, H2-TPR and NH3-TPR. The outcomes demonstrated that the Cr2O3 is the perfect targeted base catalyst adding to the facts that the hydrothermal approach is an appropriate way to create catalysts with superior properties. The textural characteristics, reducibility, and acidity of the Cr-based catalysts were all enhanced by the addition of cerium (Ce) and AC. Nevertheless, the research shows how crucial the Ce doping amount is for adjusting the physicochemical characteristics and catalytic efficacy of Cr-based catalysts. The catalyst 20Ce-Cr/AC has the maximum catalytic activity when it came to DCE combustion, where its T50 and T90 attaining of 247°C and 269.5°C, correspondingly. This is explained by the ideal Ce doping level, which improves the redox characteristics and revealed more active sites.","PeriodicalId":340494,"journal":{"name":"Journal of Engineering Research and Reports","volume":"23 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140971710","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 : 2024-05-14DOI: 10.9734/jerr/2024/v26i61168
Celestine Chepkemoi Rutto, Isaac Chepkwony, Abayomi Samuel Oke
In this study, the impact of Cu and Al2O3 nanoparticles in a water-based nanofluid are considered. The application of this can be found in biomedical sensors and drug delivery. Specifically, it investigates heat transfer in the MHD flow of two nanofluids (Cu-water and Al2O3-water) over an exponentially stretching surface. The study formulates a model and renders it dimensionless using Similarity Transformation. Numerical solutions are obtained using the MATLAB package bvp4c. The focus is on analysing the heat transfer rate variation with nanoparticle volume fraction. Results indicate that Cu-water nanofluid exhibits higher heat transfer rates and lower skin frictions compared to Al2O3-water nanofluid.
{"title":"Numerical Comparison of Cu and Al(_2) O(_3) Nanoparticles in an MHD Water-based Nanofluid","authors":"Celestine Chepkemoi Rutto, Isaac Chepkwony, Abayomi Samuel Oke","doi":"10.9734/jerr/2024/v26i61168","DOIUrl":"https://doi.org/10.9734/jerr/2024/v26i61168","url":null,"abstract":"In this study, the impact of Cu and Al2O3 nanoparticles in a water-based nanofluid are considered. The application of this can be found in biomedical sensors and drug delivery. Specifically, it investigates heat transfer in the MHD flow of two nanofluids (Cu-water and Al2O3-water) over an exponentially stretching surface. The study formulates a model and renders it dimensionless using Similarity Transformation. Numerical solutions are obtained using the MATLAB package bvp4c. The focus is on analysing the heat transfer rate variation with nanoparticle volume fraction. Results indicate that Cu-water nanofluid exhibits higher heat transfer rates and lower skin frictions compared to Al2O3-water nanofluid.","PeriodicalId":340494,"journal":{"name":"Journal of Engineering Research and Reports","volume":"93 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140978580","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 : 2024-05-09DOI: 10.9734/jerr/2024/v26i61167
MD. Ashiquzzaman, S. Alam, Abu Shufian, Protik Parvez Sheikh, Ahmed Hossain Siddiqui
In this study, a novel approach has been introduced for face recognition that is unaffected by changes in illumination. This method is based on the reflectance-luminance model and incorporates local matching using a weighted voting technique to eliminate any artifacts present in the retina images. A total of 37 different linear and nonlinear filters were tested, including high pass and low pass filters, to achieve the reflectance component of the image, which remains invariant to changes in illumination. Among these filters, the maximum filter, which is a simple filter with low computational complexity, yielded the best results in extracting the illumination invariants. It was observed that the illumination invariants obtained through this method outperformed other methods such as QI, SQI, and image enhancement techniques in terms of recognition accuracy. Importantly, the proposed method does not require any prior knowledge about the facial shape or illumination conditions and can be applied to individual images independently. Unlike many existing methods, this approach does not rely on multiple images during the training stage and does not require any parameter selection to generate the illumination invariants. To further enhance the robustness of illumination, a weighted voting system was introduced. Certain regions of the image, which may adversely affect the recognition outcome due to poor illumination, occlusion, noise, or lack of distinctive information, were identified using predefined factors such as grayscale mean, image entropy, and mutual information. The proposed method was also compared to other face recognition methods in the presence of occlusions, and it demonstrated promising results outperforming existing methods. The Python algorithm successfully detects obstructed faces, genders, and ages in videos with a face matching accuracy between 80.9% to 96.9% based on proximity.
{"title":"Novel Illumination-invariant Face Recognition Approach via Reflectance-luminance and Local Matching Model with Weighted Voting System","authors":"MD. Ashiquzzaman, S. Alam, Abu Shufian, Protik Parvez Sheikh, Ahmed Hossain Siddiqui","doi":"10.9734/jerr/2024/v26i61167","DOIUrl":"https://doi.org/10.9734/jerr/2024/v26i61167","url":null,"abstract":"In this study, a novel approach has been introduced for face recognition that is unaffected by changes in illumination. This method is based on the reflectance-luminance model and incorporates local matching using a weighted voting technique to eliminate any artifacts present in the retina images. A total of 37 different linear and nonlinear filters were tested, including high pass and low pass filters, to achieve the reflectance component of the image, which remains invariant to changes in illumination. Among these filters, the maximum filter, which is a simple filter with low computational complexity, yielded the best results in extracting the illumination invariants. It was observed that the illumination invariants obtained through this method outperformed other methods such as QI, SQI, and image enhancement techniques in terms of recognition accuracy. Importantly, the proposed method does not require any prior knowledge about the facial shape or illumination conditions and can be applied to individual images independently. Unlike many existing methods, this approach does not rely on multiple images during the training stage and does not require any parameter selection to generate the illumination invariants. To further enhance the robustness of illumination, a weighted voting system was introduced. Certain regions of the image, which may adversely affect the recognition outcome due to poor illumination, occlusion, noise, or lack of distinctive information, were identified using predefined factors such as grayscale mean, image entropy, and mutual information. The proposed method was also compared to other face recognition methods in the presence of occlusions, and it demonstrated promising results outperforming existing methods. The Python algorithm successfully detects obstructed faces, genders, and ages in videos with a face matching accuracy between 80.9% to 96.9% based on proximity.","PeriodicalId":340494,"journal":{"name":"Journal of Engineering Research and Reports","volume":" 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140996673","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}
The Internet of Things (IoT) technology facilitates automation, monitoring, and control of tangible objects and surroundings by enabling connected devices to interact and exchange data over the Internet. Developments in edge computing, blockchain, and artificial intelligence (AI) are incorporated into IoT technologies for more reliable operations. Inadequate authorization, authentication, and encryption protocols could render IoT networks insecure and open the door to illegal access and data breaches which can have terrible consequences, most notably in the healthcare industry. In this regard, to identify malicious and incursion traffic, machine learning (ML) is crucial to Internet of Things (IoT) cybersecurity. The paper proposes a framework to detect intrusion or malicious traffic in IoT-enabled different medical equipment such as medical sensors, and controllers for real-time data collection, creating communication channels and data monitoring and analysis over locally available network nodes. IoT-Flock has been utilized for both normal and malicious traffic generation in a wide dataset found by the sensors connected to IoT integrated healthcare network. The feature selection-based proposed framework has been evaluated by three distinct machine learning classifiers, KNN, RF, and DT where corresponding accuracy, sensitivity, precision, and F1-score have been measured for performance analysis. With an accuracy of 99.74%, the KNN technique performed better than the other tactics used by RF and DT regarding intrusion detection in IoT networks. The suggested framework will be helpful in developing or analyzing security solutions in IoT-integrated network systems.
{"title":"Anomaly-based Intrusion Detection System in Industrial IoT-Healthcare Environment Network","authors":"Md Maruf Rahman, Mahrima Akter Mim, Debashon Chakraborty, Zihad Hasan Joy, Nourin Nishat","doi":"10.9734/jerr/2024/v26i61166","DOIUrl":"https://doi.org/10.9734/jerr/2024/v26i61166","url":null,"abstract":"The Internet of Things (IoT) technology facilitates automation, monitoring, and control of tangible objects and surroundings by enabling connected devices to interact and exchange data over the Internet. Developments in edge computing, blockchain, and artificial intelligence (AI) are incorporated into IoT technologies for more reliable operations. Inadequate authorization, authentication, and encryption protocols could render IoT networks insecure and open the door to illegal access and data breaches which can have terrible consequences, most notably in the healthcare industry. In this regard, to identify malicious and incursion traffic, machine learning (ML) is crucial to Internet of Things (IoT) cybersecurity. The paper proposes a framework to detect intrusion or malicious traffic in IoT-enabled different medical equipment such as medical sensors, and controllers for real-time data collection, creating communication channels and data monitoring and analysis over locally available network nodes. IoT-Flock has been utilized for both normal and malicious traffic generation in a wide dataset found by the sensors connected to IoT integrated healthcare network. The feature selection-based proposed framework has been evaluated by three distinct machine learning classifiers, KNN, RF, and DT where corresponding accuracy, sensitivity, precision, and F1-score have been measured for performance analysis. With an accuracy of 99.74%, the KNN technique performed better than the other tactics used by RF and DT regarding intrusion detection in IoT networks. The suggested framework will be helpful in developing or analyzing security solutions in IoT-integrated network systems.","PeriodicalId":340494,"journal":{"name":"Journal of Engineering Research and Reports","volume":" 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140998470","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 : 2024-05-07DOI: 10.9734/jerr/2024/v26i61165
O. Alabi, Gideon Giwa, Francis Gitau, Gbadamosi Yemisi Elizabeth
Cassiterite has been an essential source of tin; its exploration and exploitation have become a priority worldwide. The effective beneficiation of cassiterite depends mainly on its grindability and effective liberation. The Modified Bond's grindability test is a method used to determine the work index, which is crucial in estimating the energy needed to grind an ore. This is crucial during mineral processing, as a slight deviation would affect the company's operating expenditure (OPEX). This study investigates the work index for Farin-Lamba cassiterite with reference to silica sand sourced from Igbokoda. The test ore (cassiterite) was analyzed using Energy Dispersed X-ray fluorescence spectrometer (ED-XRS), Petrographic Analysis, and Scanning Electron Microscope equipped with an Energy Dispersive Spectrum (SEM-EDS) to understand their chemical and mineralogical characteristics in relation to their grindability. The test ore and the reference material (silica sand) underwent comminution using 500g of each to a 100% passing 500 μm array of sieves arranged in (√2) series from 500μm to 63μm onto an automatic sieve shaker. The chemical and mineralogical analysis revealed the presence of gangue such as SiO2 and Al2O3, which increases the energy needed during communition; further, the presence of rough and large grain size in the ore also increases the energy needed for communition. The results were subjected to Gaudin Schuman's equation to determine the ore's work index, which was 14.664 KWh/ton for the test ore, which is standard for cassiterite, which ranges from 10-15 kWh/ton. Further, the energy needed for comminution was calculated to be 33.7272 Kwh, providing valuable insights into the energy efficiency of the grinding process. The evaluation of the grindability of Farin-Lamba cassiterite in relation to the reference ore not only contributes toward understanding the ore processing dynamics but also provides information needed for the optimization of energy consumed during the process of tin oxide production.
{"title":"Investigating the Characterization and Grindability Behaviour of Farin-lamba Cassiterite toward Effective Tin Oxide Production","authors":"O. Alabi, Gideon Giwa, Francis Gitau, Gbadamosi Yemisi Elizabeth","doi":"10.9734/jerr/2024/v26i61165","DOIUrl":"https://doi.org/10.9734/jerr/2024/v26i61165","url":null,"abstract":"Cassiterite has been an essential source of tin; its exploration and exploitation have become a priority worldwide. The effective beneficiation of cassiterite depends mainly on its grindability and effective liberation. The Modified Bond's grindability test is a method used to determine the work index, which is crucial in estimating the energy needed to grind an ore. This is crucial during mineral processing, as a slight deviation would affect the company's operating expenditure (OPEX). This study investigates the work index for Farin-Lamba cassiterite with reference to silica sand sourced from Igbokoda. The test ore (cassiterite) was analyzed using Energy Dispersed X-ray fluorescence spectrometer (ED-XRS), Petrographic Analysis, and Scanning Electron Microscope equipped with an Energy Dispersive Spectrum (SEM-EDS) to understand their chemical and mineralogical characteristics in relation to their grindability. The test ore and the reference material (silica sand) underwent comminution using 500g of each to a 100% passing 500 μm array of sieves arranged in (√2) series from 500μm to 63μm onto an automatic sieve shaker. The chemical and mineralogical analysis revealed the presence of gangue such as SiO2 and Al2O3, which increases the energy needed during communition; further, the presence of rough and large grain size in the ore also increases the energy needed for communition. The results were subjected to Gaudin Schuman's equation to determine the ore's work index, which was 14.664 KWh/ton for the test ore, which is standard for cassiterite, which ranges from 10-15 kWh/ton. Further, the energy needed for comminution was calculated to be 33.7272 Kwh, providing valuable insights into the energy efficiency of the grinding process. The evaluation of the grindability of Farin-Lamba cassiterite in relation to the reference ore not only contributes toward understanding the ore processing dynamics but also provides information needed for the optimization of energy consumed during the process of tin oxide production.","PeriodicalId":340494,"journal":{"name":"Journal of Engineering Research and Reports","volume":"20 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141005984","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 : 2024-05-07DOI: 10.9734/jerr/2024/v26i61164
L. Emembolu, F. U. Iwuchukwu, C. C. Ejiofor, J. J. Ajali, C. E. Chinyelu
The aim of this work is to investigate the corrosion inhibition of B. eurycoma leaves extract as a natural inhibitor for mild steel corrosion in 3 M H2SO4 solution. The corrosion inhibitory activity was analyzed by electrochemical impedance spectroscopy (EIS) and potentiodynamic polarization. The surface roughness and its properties through scanning electron microscopy (SEM). The obtained result from EIS divulges that the gradual increase in inhibitor concentration and time of immersion leads to progressive increase in inhibition efficiency. At the end of 8 hours immersion time and inhibitor concentration of 1000 mg/l the highest inhibition efficiency of (88%) was obtained. The potentiodynamic polarization results indicated that addition of B. eurycoma leaves extracts hindered the reaction rates of anodic and cathodic reactions thereby performing as mixed- type inhibitor. The corrosion current density also revealed that in the presence of B. eurycoma leaves extracts the value of corrosion density reduced considerably from 265.2 (mu) A/cm2 for sample without inhibitor to 67.8 (mu) A/cm2 for sample with inhibitor. Again, it was observed that in the presence B. eurycoma leaves extracts the mechanism of hydrogen (effervescence) evolution was unique whereas the anodic dissolution of iron mechanism experienced the impact of the inhibitor. SEM inspection revealed that the mild steel surface showed smoother and lower damage in the presence of inhibitor. Obvious correlations were found between corrosion inhibition efficiency and some quantum chemical parameters such as energy of highest occupied molecular orbital (EHOMO), energy of lowest unoccupied molecular orbital (ELUMO), energy gap (EL–H) and electronic density etc. The obtained results were further elucidated with frontier molecular orbital theory.
本研究的目的是探讨 B. eurycoma 叶提取物作为一种天然抑制剂在 3 M H2SO4 溶液中对低碳钢腐蚀的抑制作用。通过电化学阻抗光谱（EIS）和电位极化对缓蚀活性进行了分析。通过扫描电子显微镜（SEM）分析了表面粗糙度及其特性。 电化学阻抗谱分析的结果表明，随着抑制剂浓度和浸泡时间的逐渐增加，抑制效率也逐渐提高。在浸泡时间为 8 小时、抑制剂浓度为 1000 毫克/升时，抑制效率最高（88%）。电位极化结果表明，加入 B. eurycoma 叶提取物会阻碍阳极反应和阴极反应的速率，从而起到混合型抑制剂的作用。腐蚀电流密度也表明，在 B. eurycoma 叶提取物存在的情况下，腐蚀密度值从没有加入抑制剂的样品的 265.2 (mu) A/cm2 大大降低到加入抑制剂的样品的 67.8 (mu) A/cm2 。再次观察发现，在有欧鼠李叶提取物存在的情况下，氢气（泡腾作用）的演化机理是独特的，而铁的阳极溶解机理则经历了抑制剂的影响。扫描电子显微镜（SEM）检查显示，在抑制剂存在的情况下，低碳钢表面更光滑，损坏程度更低。研究发现，缓蚀效率与一些量子化学参数之间存在明显的相关性，如最高占有分子轨道能量（EHOMO）、最低未占有分子轨道能量（ELUMO）、能隙（EL-H）和电子密度等。前沿分子轨道理论进一步阐明了所获得的结果。
{"title":"Corrosion Inhibition Performance of Brachystegia eurycoma Leaf Extract on Mild Steel in Acid Media","authors":"L. Emembolu, F. U. Iwuchukwu, C. C. Ejiofor, J. J. Ajali, C. E. Chinyelu","doi":"10.9734/jerr/2024/v26i61164","DOIUrl":"https://doi.org/10.9734/jerr/2024/v26i61164","url":null,"abstract":"The aim of this work is to investigate the corrosion inhibition of B. eurycoma leaves extract as a natural inhibitor for mild steel corrosion in 3 M H2SO4 solution. The corrosion inhibitory activity was analyzed by electrochemical impedance spectroscopy (EIS) and potentiodynamic polarization. The surface roughness and its properties through scanning electron microscopy (SEM). The obtained result from EIS divulges that the gradual increase in inhibitor concentration and time of immersion leads to progressive increase in inhibition efficiency. At the end of 8 hours immersion time and inhibitor concentration of 1000 mg/l the highest inhibition efficiency of (88%) was obtained. The potentiodynamic polarization results indicated that addition of B. eurycoma leaves extracts hindered the reaction rates of anodic and cathodic reactions thereby performing as mixed- type inhibitor. The corrosion current density also revealed that in the presence of B. eurycoma leaves extracts the value of corrosion density reduced considerably from 265.2 (mu) A/cm2 for sample without inhibitor to 67.8 (mu) A/cm2 for sample with inhibitor. Again, it was observed that in the presence B. eurycoma leaves extracts the mechanism of hydrogen (effervescence) evolution was unique whereas the anodic dissolution of iron mechanism experienced the impact of the inhibitor. SEM inspection revealed that the mild steel surface showed smoother and lower damage in the presence of inhibitor. Obvious correlations were found between corrosion inhibition efficiency and some quantum chemical parameters such as energy of highest occupied molecular orbital (EHOMO), energy of lowest unoccupied molecular orbital (ELUMO), energy gap (EL–H) and electronic density etc. The obtained results were further elucidated with frontier molecular orbital theory.","PeriodicalId":340494,"journal":{"name":"Journal of Engineering Research and Reports","volume":"73 s312","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141002497","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 : 2024-05-06DOI: 10.9734/jerr/2024/v26i61163
Yaomin Ma
The frequent occurrence of drowning accidents has brought a large negative impact on the whole society. In response to this situation, in order to improve the traditional waterside supervision and protection facilities and reduce drowning accidents, this paper illustrates an all-in-one drowning prevention and monitoring system with a modular design, and carries out a large number of experiments and optimisation of the system's functions such as danger warning and real-time monitoring. The results show that the system can efficiently achieve the functions of alarm and distress in drowning accidents and reduce the occurrence of drowning accidents.
{"title":"Design of an All-in-One Drowning Prevention and Monitoring System","authors":"Yaomin Ma","doi":"10.9734/jerr/2024/v26i61163","DOIUrl":"https://doi.org/10.9734/jerr/2024/v26i61163","url":null,"abstract":"The frequent occurrence of drowning accidents has brought a large negative impact on the whole society. In response to this situation, in order to improve the traditional waterside supervision and protection facilities and reduce drowning accidents, this paper illustrates an all-in-one drowning prevention and monitoring system with a modular design, and carries out a large number of experiments and optimisation of the system's functions such as danger warning and real-time monitoring. The results show that the system can efficiently achieve the functions of alarm and distress in drowning accidents and reduce the occurrence of drowning accidents.","PeriodicalId":340494,"journal":{"name":"Journal of Engineering Research and Reports","volume":"42 25","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141010340","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 : 2024-05-04DOI: 10.9734/jerr/2024/v26i61160
O. O. Olaniyi, Olajide Oyebola Omogoroye, Folashade Gloria Olaniyi, Adegbenga Ismaila Alao, Tunbosun Oyewale Oladoyinbo
This research paper explores the integration of Enterprise Risk Management (ERM), the ISO 27001 standard, and mobile forensics methodologies as a comprehensive framework for enhancing digital security measures within modern business ecosystems. Employing a quantitative research design, this paper utilized a survey methodology, gathering data from 372 professionals across various sectors including risk management, IT/security, and forensic analysis. The analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the research hypotheses and assess the impact of the integrated approach on organizational digital security capabilities. The findings reveal a significant positive effect of integrating ERM, ISO 27001, and mobile forensics on an organization’s ability to manage digital risks effectively. Specifically, the integrated approach was found to enhance strategic digital security management, improve the identification, assessment, and mitigation of digital risks, strengthen information security management practices, and elevate the effectiveness and efficiency of digital crime investigation processes. These outcomes underscore the value of a cohesive strategy that leverages the strengths of ERM, ISO 27001, and mobile forensics in addressing the complex and interconnected digital threat landscape. Based on the results, the study recommends adopting a holistic security framework, investing in continuous professional development, leveraging technological advancements for proactive security management, and fostering a culture of security and collaboration. Such measures are crucial for organizations aiming to enhance their resilience against cyber threats and protect their digital assets in the face of sophisticated cyber-attacks. This research contributes to the field of cybersecurity by providing empirical evidence on the benefits of an integrated approach to digital security, offering practical guidelines for organizations seeking to improve their digital security measures, and highlighting the need for continuous adaptation and collaboration in the fight against cyber threats.
{"title":"CyberFusion Protocols: Strategic Integration of Enterprise Risk Management, ISO 27001, and Mobile Forensics for Advanced Digital Security in the Modern Business Ecosystem","authors":"O. O. Olaniyi, Olajide Oyebola Omogoroye, Folashade Gloria Olaniyi, Adegbenga Ismaila Alao, Tunbosun Oyewale Oladoyinbo","doi":"10.9734/jerr/2024/v26i61160","DOIUrl":"https://doi.org/10.9734/jerr/2024/v26i61160","url":null,"abstract":"This research paper explores the integration of Enterprise Risk Management (ERM), the ISO 27001 standard, and mobile forensics methodologies as a comprehensive framework for enhancing digital security measures within modern business ecosystems. Employing a quantitative research design, this paper utilized a survey methodology, gathering data from 372 professionals across various sectors including risk management, IT/security, and forensic analysis. The analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the research hypotheses and assess the impact of the integrated approach on organizational digital security capabilities. The findings reveal a significant positive effect of integrating ERM, ISO 27001, and mobile forensics on an organization’s ability to manage digital risks effectively. Specifically, the integrated approach was found to enhance strategic digital security management, improve the identification, assessment, and mitigation of digital risks, strengthen information security management practices, and elevate the effectiveness and efficiency of digital crime investigation processes. These outcomes underscore the value of a cohesive strategy that leverages the strengths of ERM, ISO 27001, and mobile forensics in addressing the complex and interconnected digital threat landscape. Based on the results, the study recommends adopting a holistic security framework, investing in continuous professional development, leveraging technological advancements for proactive security management, and fostering a culture of security and collaboration. Such measures are crucial for organizations aiming to enhance their resilience against cyber threats and protect their digital assets in the face of sophisticated cyber-attacks. This research contributes to the field of cybersecurity by providing empirical evidence on the benefits of an integrated approach to digital security, offering practical guidelines for organizations seeking to improve their digital security measures, and highlighting the need for continuous adaptation and collaboration in the fight against cyber threats.","PeriodicalId":340494,"journal":{"name":"Journal of Engineering Research and Reports","volume":"16 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141014535","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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