The common issue of user organization, allocation of resources, and base station (BS) implementation occurs when a network is heterogeneous (Het-Net) is helped by several radio-accessing technologies (Multi-RAT). Real-time user situations make it difficult to allocate resources in Het-Net optimally while guar-anteeing that every user receives the smallest necessary information rate. Due to the current enormous development of wireless network systems and the exponential growth of data congestion, there is legitimate worry about the corresponding sharp rise in energy usage. In order to tackle these issues, the study here provides a brand-new method that combines an established and effective metaheuristic optimization approach. Consequently, the importance of energy-effective layout in multi-RAT systems is rising. Numerous radio-accessing technologies (RATs) have developed and are currently coexisting in order to serve the varied needs for the network’s infrastructure and mobile user equipment (UE). These RATs operate with different network settings and allocations of resources. A promising technique to address the issue of spectrum scarcity is cognitive radio (CR) technology. System capacity may be increased via multi-radio accessing technologies (multi-RAT). In order to increase the efficiency of spectrum and network flexibility for subsequent wireless systems, multi-RAT integrated into a cognitive Radio Network (CRN) is a potential model. Considering the novel CRN concept used in this research, in which the principal users make up the principal user channels. Concentrate the study on the CR’s Energy Effective Resources Allocation (EERA) challenge. The research recommends using the orthogonal frequency division multiple access with two-tier cross-over genetics algorithm-based searching approach (OFDMA-TTCGA) to discover a solution with the optimum processing power and bandwidth. The fundamental and crucial method employed by CR to locate unutilized airwaves is spectral sense. A spectrum detector employing the detection of energy has been suggested. Simulation outcomes demonstrate the stability and quicker convergence of the suggested approach. The effectiveness of energy use may be greatly improved with the suggestion. The results of simulations demonstrate that the suggested approaches can get performances close to the ideal solution with a great deal simpler structure and a greater efficiency of energy than a traditional spectral-efficiency-based method.
当异构网络（Het-Net）在多种无线接入技术（Multi-RAT）的帮助下运行时，用户组织、资源分配和基站（BS）实施是一个共同的问题。由于用户的实时情况，很难在 Het-Net 中优化分配资源，同时保证每个用户都能获得最小的必要信息速率。由于当前无线网络系统的巨大发展和数据拥塞的指数级增长，人们有理由担心相应的能源使用量会急剧上升。为了解决这些问题，本研究提供了一种全新的方法，它结合了一种成熟有效的元启发式优化方法。因此，在多 RAT 系统中进行高能效布局的重要性日益凸显。为了满足网络基础设施和移动用户设备（UE）的不同需求，许多无线接入技术（RAT）已经发展起来，目前正在共存。这些 RAT 以不同的网络设置和资源分配方式运行。认知无线电（CR）技术是解决频谱稀缺问题的一项有前途的技术。多无线电接入技术（multi-RAT）可提高系统容量。为了提高后续无线系统的频谱效率和网络灵活性，将多 RAT 集成到认知无线电网络（CRN）中是一种可行的模式。考虑到本研究中使用的新型 CRN 概念，其中主要用户构成主要用户信道。将研究重点放在 CR 的能源有效资源分配（EERA）挑战上。研究建议使用基于两层交叉遗传算法的正交频分多址搜索方法（OFDMA-TTCGA）来发现具有最佳处理能力和带宽的解决方案。CR 用来定位未利用电波的基本和关键方法是频谱感应。我们提出了一种利用能量检测的频谱检测器。仿真结果表明，所建议的方法具有稳定性和快速收敛性。该建议可大大提高能源利用效率。模拟结果表明，与传统的基于频谱效率的方法相比，建议的方法结构更简单，能量效率更高，性能更接近理想解决方案。
{"title":"Energy Efficient Resources Allocation on Multi Rat Cognitive Radio Network Using Nanotechnology","authors":"Sabeenian Royappan Savarimuthu, Nandhini Thenmozhi Jaisankar, Manjunathan Alagarsamy, Sabeenian Royappan, Savarimuthu","doi":"10.17756/nwj.2023-s3-063","DOIUrl":"https://doi.org/10.17756/nwj.2023-s3-063","url":null,"abstract":"The common issue of user organization, allocation of resources, and base station (BS) implementation occurs when a network is heterogeneous (Het-Net) is helped by several radio-accessing technologies (Multi-RAT). Real-time user situations make it difficult to allocate resources in Het-Net optimally while guar-anteeing that every user receives the smallest necessary information rate. Due to the current enormous development of wireless network systems and the exponential growth of data congestion, there is legitimate worry about the corresponding sharp rise in energy usage. In order to tackle these issues, the study here provides a brand-new method that combines an established and effective metaheuristic optimization approach. Consequently, the importance of energy-effective layout in multi-RAT systems is rising. Numerous radio-accessing technologies (RATs) have developed and are currently coexisting in order to serve the varied needs for the network’s infrastructure and mobile user equipment (UE). These RATs operate with different network settings and allocations of resources. A promising technique to address the issue of spectrum scarcity is cognitive radio (CR) technology. System capacity may be increased via multi-radio accessing technologies (multi-RAT). In order to increase the efficiency of spectrum and network flexibility for subsequent wireless systems, multi-RAT integrated into a cognitive Radio Network (CRN) is a potential model. Considering the novel CRN concept used in this research, in which the principal users make up the principal user channels. Concentrate the study on the CR’s Energy Effective Resources Allocation (EERA) challenge. The research recommends using the orthogonal frequency division multiple access with two-tier cross-over genetics algorithm-based searching approach (OFDMA-TTCGA) to discover a solution with the optimum processing power and bandwidth. The fundamental and crucial method employed by CR to locate unutilized airwaves is spectral sense. A spectrum detector employing the detection of energy has been suggested. Simulation outcomes demonstrate the stability and quicker convergence of the suggested approach. The effectiveness of energy use may be greatly improved with the suggestion. The results of simulations demonstrate that the suggested approaches can get performances close to the ideal solution with a great deal simpler structure and a greater efficiency of energy than a traditional spectral-efficiency-based method.","PeriodicalId":36802,"journal":{"name":"NanoWorld Journal","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139320201","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}
Zirconia (ZrO 2 ) and silica (SiO 2 ) nanoparticles suspended in water are the focus of this investigation on the influence of environmental variables on the dynamic viscosity of these nanofluids. Two different viscometers (a falling ball and a capillary) were used to measure the range of temperatures from 30 to 60 °C and the percentage of particles from 4 to 15.4%. The results demonstrate that, similar to their base fluids, nanofluids’ viscosity reduces as temperature rises. Surfactants are added to nanofluids to improve their stability at room temperature; however, this is likely at the expense of an increase in viscosity. However, the modified Krieger-Dougherty relation provides reasonably accurate estimation of nanofluid viscosity within a narrow limit of solid size of particle to cumulative size, while relations attained from the lenient liquid concept, like Einstein’s and Bachelor’s, fail to predict nanofluid viscosity for solid concentrations above 1.5 wt.%.
{"title":"Effect of Environmental Factors on Dynamic Viscosity of Zirconia and Silica Nanofluids: Experimental Insights and Theoretical Predictions","authors":"Salam, Firos Abdul, Pramod Sridhara, Jayanthi Narayanaswamy, Vasanthakumar Ramalingasamy, Saravanan Vasude-van, Nantha-kumar Sivasamy, Mayakannan Selvaraju, Ramadoss Yokeswaran","doi":"10.17756/nwj.2023-s3-060","DOIUrl":"https://doi.org/10.17756/nwj.2023-s3-060","url":null,"abstract":"Zirconia (ZrO 2 ) and silica (SiO 2 ) nanoparticles suspended in water are the focus of this investigation on the influence of environmental variables on the dynamic viscosity of these nanofluids. Two different viscometers (a falling ball and a capillary) were used to measure the range of temperatures from 30 to 60 °C and the percentage of particles from 4 to 15.4%. The results demonstrate that, similar to their base fluids, nanofluids’ viscosity reduces as temperature rises. Surfactants are added to nanofluids to improve their stability at room temperature; however, this is likely at the expense of an increase in viscosity. However, the modified Krieger-Dougherty relation provides reasonably accurate estimation of nanofluid viscosity within a narrow limit of solid size of particle to cumulative size, while relations attained from the lenient liquid concept, like Einstein’s and Bachelor’s, fail to predict nanofluid viscosity for solid concentrations above 1.5 wt.%.","PeriodicalId":36802,"journal":{"name":"NanoWorld Journal","volume":"49 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139320249","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 : 2023-10-12DOI: 10.17756/nwj.2023-s3-062
{"title":"A Design of Adaptive Space Time Frequency MIMO-OFDM with Nanotechnology","authors":"","doi":"10.17756/nwj.2023-s3-062","DOIUrl":"https://doi.org/10.17756/nwj.2023-s3-062","url":null,"abstract":"","PeriodicalId":36802,"journal":{"name":"NanoWorld Journal","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139319845","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 : 2023-10-12DOI: 10.17756/nwj.2023-s3-065
{"title":"The Impact of Combination of Al2O3 Nanoparticles with Fly Ash and Glass Powder in Concrete","authors":"","doi":"10.17756/nwj.2023-s3-065","DOIUrl":"https://doi.org/10.17756/nwj.2023-s3-065","url":null,"abstract":"","PeriodicalId":36802,"journal":{"name":"NanoWorld Journal","volume":"46 4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139320036","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 : 2023-10-11DOI: 10.17756/nwj.2023-s3-055
{"title":"Enhancement of Mechanical and Corrosion Properties of AA5128 with Nano Boron Carbide Particulates Using Ultrasonic Cavitation Assisted Stir Casting Technique","authors":"","doi":"10.17756/nwj.2023-s3-055","DOIUrl":"https://doi.org/10.17756/nwj.2023-s3-055","url":null,"abstract":"","PeriodicalId":36802,"journal":{"name":"NanoWorld Journal","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139320464","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 researcher used a cell segmentation technique in conjunction with other image analysis methods to quantitatively retrieve and compute the cellular microstructural structures in a sub-grain size of silicon carbide (SiC)-reinforced AA2219 made by powder fusion bed (size 0.5 - 1µm). Over 83 geometric features were retrieved and statistically analyzed using ML (Machine learning) techniques to examine the structure-property relationships in SiC-reinforced AlSi20Mg nanocomposites. These sub-grain cellular microstructure properties were utilized to develop hardness and relative mass density analytical models. Using principal component analysis (PCA), authors could narrow down the three variables. While all of the AlSi20Mg nanocomposite samples had identical Al-Si eutectic microstructures, the mechanical properties, such as hardness and relative mass density, varied widely depending on the laser parameters used to create them. Extra Tress regression models that attempted to predict hardness had a close error rate of 2.47%. Using a regression model based on Decision Trees, authors could predict relative mass density to within 0.42 standard deviations. The established models are shown to be capable of predicting the relative hardness and relative mass density of AlSi20Mg nanocomposites. The structure identified in this study has applications for controlling the mechanical properties of PFB (powder fusion beds) and could be applied to other additively manufactured alloys and composites.
{"title":"Machine Learning-based Investigation of Wear and Frictional Behavior in Graphite-reinforced Aluminum Nanocomposites","authors":"Sathishkumar Arumugam, Sachin Kumar, Pramod Sridhara, Srinivasan Raju, Ashwin Prabhu Gnanasekaran, Nantha-kumar Sivasamy, Thangarajan Sivasankaran Senthilkumar","doi":"10.17756/nwj.2023-s3-054","DOIUrl":"https://doi.org/10.17756/nwj.2023-s3-054","url":null,"abstract":"The researcher used a cell segmentation technique in conjunction with other image analysis methods to quantitatively retrieve and compute the cellular microstructural structures in a sub-grain size of silicon carbide (SiC)-reinforced AA2219 made by powder fusion bed (size 0.5 - 1µm). Over 83 geometric features were retrieved and statistically analyzed using ML (Machine learning) techniques to examine the structure-property relationships in SiC-reinforced AlSi20Mg nanocomposites. These sub-grain cellular microstructure properties were utilized to develop hardness and relative mass density analytical models. Using principal component analysis (PCA), authors could narrow down the three variables. While all of the AlSi20Mg nanocomposite samples had identical Al-Si eutectic microstructures, the mechanical properties, such as hardness and relative mass density, varied widely depending on the laser parameters used to create them. Extra Tress regression models that attempted to predict hardness had a close error rate of 2.47%. Using a regression model based on Decision Trees, authors could predict relative mass density to within 0.42 standard deviations. The established models are shown to be capable of predicting the relative hardness and relative mass density of AlSi20Mg nanocomposites. The structure identified in this study has applications for controlling the mechanical properties of PFB (powder fusion beds) and could be applied to other additively manufactured alloys and composites.","PeriodicalId":36802,"journal":{"name":"NanoWorld Journal","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139320652","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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