This study delves into biodiesel synthesis from non-edible oils and algae oil sources using Response Surface Methodology(RSM)and an Artificial Neural Network(ANN)model to optimize biodiesel yield.Blend of C.vulgaris a...This study delves into biodiesel synthesis from non-edible oils and algae oil sources using Response Surface Methodology(RSM)and an Artificial Neural Network(ANN)model to optimize biodiesel yield.Blend of C.vulgaris and Karanja oils is utilized,aiming to reduce free fatty acid content to 1%through single-step transesterification.Optimization reveals peak biodiesel yield conditions:1%catalyst quantity,91.47 min reaction time,56.86℃reaction temperature,and 8.46:1 methanol to oil molar ratio.The ANN model outperforms RSM in yield prediction accuracy.Environmental impact assessment yields an E-factor of 0.0251 at maximum yield,indicating responsible production with minimal waste.Economic analysis reveals significant cost savings:30%-50%reduction in raw material costs by using non-edible oils,10%-15%increase in production efficiency,20%reduction in catalyst costs,and 15%-20%savings in energy consumption.The optimized process reduces waste disposal costs by 10%-15%,enhancing overall economic viability.Overall,the widespread adoption of biodiesel offers economic,environmental,and social benefits to a diverse range of stakeholders,including farmers,producers,consumers,governments,environmental organizations,and the transportation industry.Collaboration among these stakeholders is essential for realizing the full potential of biodiesel as a sustainable energy solution.展开更多
In pursuit of enhancing the Wireless Sensor Networks(WSNs)energy efficiency and operational lifespan,this paper delves into the domain of energy-efficient routing protocols.InWSNs,the limited energy resources of Senso...In pursuit of enhancing the Wireless Sensor Networks(WSNs)energy efficiency and operational lifespan,this paper delves into the domain of energy-efficient routing protocols.InWSNs,the limited energy resources of Sensor Nodes(SNs)are a big challenge for ensuring their efficient and reliable operation.WSN data gathering involves the utilization of a mobile sink(MS)to mitigate the energy consumption problem through periodic network traversal.The mobile sink(MS)strategy minimizes energy consumption and latency by visiting the fewest nodes or predetermined locations called rendezvous points(RPs)instead of all cluster heads(CHs).CHs subsequently transmit packets to neighboring RPs.The unique determination of this study is the shortest path to reach RPs.As the mobile sink(MS)concept has emerged as a promising solution to the energy consumption problem in WSNs,caused by multi-hop data collection with static sinks.In this study,we proposed two novel hybrid algorithms,namely“ Reduced k-means based on Artificial Neural Network”(RkM-ANN)and“Delay Bound Reduced kmeans with ANN”(DBRkM-ANN)for designing a fast,efficient,and most proficient MS path depending upon rendezvous points(RPs).The first algorithm optimizes the MS’s latency,while the second considers the designing of delay-bound paths,also defined as the number of paths with delay over bound for the MS.Both methods use a weight function and k-means clustering to choose RPs in a way that maximizes efficiency and guarantees network-wide coverage.In addition,a method of using MS scheduling for efficient data collection is provided.Extensive simulations and comparisons to several existing algorithms have shown the effectiveness of the suggested methodologies over a wide range of performance indicators.展开更多
The research examines fluid behavior in a porous box-shaped enclosure.The fluid contains nanoscale particles and swimming microbes and is subject to magnetic forces at an angle.Natural circulation driven by biological...The research examines fluid behavior in a porous box-shaped enclosure.The fluid contains nanoscale particles and swimming microbes and is subject to magnetic forces at an angle.Natural circulation driven by biological factors is investigated.The analysis combines a traditional numerical approach with machine learning techniques.Mathematical equations describing the system are transformed into a dimensionless form and then solved using computational methods.The artificial neural network(ANN)model,trained with the Levenberg-Marquardt method,accurately predicts(Nu)values,showing high correlation(R=1),low mean squared error(MSE),and minimal error clustering.Parametric analysis reveals significant effects of parameters,length and location of source(B),(D),heat generation/absorption coefficient(Q),and porosity parameter(ε).Increasing the cooling area length(B)reduces streamline intensity and local Nusselt and Sherwood numbers,while decreasing isotherms,isoconcentrations,and micro-rotation.The Bejan number(Be+)decreases with increasing(B),whereas(Be+++),and global entropy(e+++)increase.Variations in(Q)slightly affect streamlines but reduce isotherm intensity and average Nusselt numbers.Higher(D)significantly impacts isotherms,iso-concentrations,andmicro-rotation,altering streamline contours and local Bejan number distribution.Increased(ε)enhances streamline strength and local Nusselt number profiles but has mixed effects on average Nusselt numbers.These findings highlight the complex interactions between cooling area length,fluid flow,and heat transfer properties.By combining finite volume method(FVM)with machine learning technique,this study provides valuable insights into the complex interactions between key parameters and heat transfer,contributing to the development of more efficient designs in applications such as cooling systems,energy storage,and bioengineering.展开更多
文章研究探讨了人工神经网络(ANN)在网络推荐算法领域的运用。ANN算法具备将用户和物品的特征向量映射至低维空间的能力,通过衡量用户过往行为与潜在推荐对象间的相似程度,迅速锁定用户可能青睐的内容,进而实现个性化推荐。研究过程中,...文章研究探讨了人工神经网络(ANN)在网络推荐算法领域的运用。ANN算法具备将用户和物品的特征向量映射至低维空间的能力,通过衡量用户过往行为与潜在推荐对象间的相似程度,迅速锁定用户可能青睐的内容,进而实现个性化推荐。研究过程中,我们运用ANN方法对用户的浏览、收藏及加购行为的权值展开了动态解析与计算。重点关注于多层人工神经网络模型,购买模型和基于距离的向量匹配算法相结合,并在模型中提出高效且准确的个性化推荐系统。提出感知器模型,进而提出多层人工神经网络,进行网络预测,不仅解决了信息茧房与过度推荐的问题,还解决了用户反馈难以进行个性化推荐的问题,并以此提出一种基于距离的向量匹配算法,针对不同用户推送个性化的商品。实验阶段,我们依据用户对商品的收藏频次、购买相关商品的次数以及浏览该商品及其相关商品的数量,来预测用户即将购买的商品数量及其在各商品上的浏览时长。将网络预测的结果与实际测试集数据进行比对后,我们发现网络预测与实际情况展现出较高的吻合度。随后采用基于距离的向量匹配技术,针对不同用户推送个性化的商品信息。最后进行对比分析,与其他网络预测方法比较,突出本文方法的优势。In this paper, We studies the application of ANN (artificial neural network) method in network recommendation algorithm. ANN algorithm can map the feature vectors of users and objects into the low dimensional space, by calculating the similarity between user historical behavior and candidate recommendation items, quickly find the content that the user may be interested in, and realize personalized recommendation.In the process of research, we used the ANN method to dynamically analyse and calculate the weights of users’ browsing, collection and purchase behaviours. This paper focuses on the combination of multi-layer artificial neural network model, purchase model and distance-based vector matching algorithm, and proposes an efficient and accurate personalised recommendation system in the model. The perceptron model is proposed, and then a multi-layer artificial neural network is proposed for network prediction, which not only solves the problem of information cocoon and over-recommendation, but also solves the problem that it is difficult to make personalised recommendations based on user feedback, and proposes a distance-based vector matching algorithm to push personalised products for different users. At the same time, making personalized recommendations using ANN methods can gain new cognition, create new wisdom, and produce more valuable decisions. Explore the consumer needs of the users from the historical data, explore the new cognition from the old data, create the new wisdom from the new cognition combined with the machine learning algorithms, and finally help the users to find the goods they are interested in, and present the most suitable products to the users. Finally, a comparative analysis is conducted to highlight the advantages of our method compared to other network prediction methods.展开更多
基金the financial support provided for this research project entitled“Enhancement of Cold Flow Properties of Waste Cooking Biodiesel and Diesel”under the File Number A/RD/RP-2/345 for the above publication.
文摘This study delves into biodiesel synthesis from non-edible oils and algae oil sources using Response Surface Methodology(RSM)and an Artificial Neural Network(ANN)model to optimize biodiesel yield.Blend of C.vulgaris and Karanja oils is utilized,aiming to reduce free fatty acid content to 1%through single-step transesterification.Optimization reveals peak biodiesel yield conditions:1%catalyst quantity,91.47 min reaction time,56.86℃reaction temperature,and 8.46:1 methanol to oil molar ratio.The ANN model outperforms RSM in yield prediction accuracy.Environmental impact assessment yields an E-factor of 0.0251 at maximum yield,indicating responsible production with minimal waste.Economic analysis reveals significant cost savings:30%-50%reduction in raw material costs by using non-edible oils,10%-15%increase in production efficiency,20%reduction in catalyst costs,and 15%-20%savings in energy consumption.The optimized process reduces waste disposal costs by 10%-15%,enhancing overall economic viability.Overall,the widespread adoption of biodiesel offers economic,environmental,and social benefits to a diverse range of stakeholders,including farmers,producers,consumers,governments,environmental organizations,and the transportation industry.Collaboration among these stakeholders is essential for realizing the full potential of biodiesel as a sustainable energy solution.
基金Research Supporting Project Number(RSP2024R421),King Saud University,Riyadh,Saudi Arabia.
文摘In pursuit of enhancing the Wireless Sensor Networks(WSNs)energy efficiency and operational lifespan,this paper delves into the domain of energy-efficient routing protocols.InWSNs,the limited energy resources of Sensor Nodes(SNs)are a big challenge for ensuring their efficient and reliable operation.WSN data gathering involves the utilization of a mobile sink(MS)to mitigate the energy consumption problem through periodic network traversal.The mobile sink(MS)strategy minimizes energy consumption and latency by visiting the fewest nodes or predetermined locations called rendezvous points(RPs)instead of all cluster heads(CHs).CHs subsequently transmit packets to neighboring RPs.The unique determination of this study is the shortest path to reach RPs.As the mobile sink(MS)concept has emerged as a promising solution to the energy consumption problem in WSNs,caused by multi-hop data collection with static sinks.In this study,we proposed two novel hybrid algorithms,namely“ Reduced k-means based on Artificial Neural Network”(RkM-ANN)and“Delay Bound Reduced kmeans with ANN”(DBRkM-ANN)for designing a fast,efficient,and most proficient MS path depending upon rendezvous points(RPs).The first algorithm optimizes the MS’s latency,while the second considers the designing of delay-bound paths,also defined as the number of paths with delay over bound for the MS.Both methods use a weight function and k-means clustering to choose RPs in a way that maximizes efficiency and guarantees network-wide coverage.In addition,a method of using MS scheduling for efficient data collection is provided.Extensive simulations and comparisons to several existing algorithms have shown the effectiveness of the suggested methodologies over a wide range of performance indicators.
基金Deanship of Scientific Research at King Khalid University,Abha,Saudi Arabia,for funding this work through theResearch Group Project underGrant Number(RGP.2/610/45)funded by the Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2024R102)PrincessNourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The research examines fluid behavior in a porous box-shaped enclosure.The fluid contains nanoscale particles and swimming microbes and is subject to magnetic forces at an angle.Natural circulation driven by biological factors is investigated.The analysis combines a traditional numerical approach with machine learning techniques.Mathematical equations describing the system are transformed into a dimensionless form and then solved using computational methods.The artificial neural network(ANN)model,trained with the Levenberg-Marquardt method,accurately predicts(Nu)values,showing high correlation(R=1),low mean squared error(MSE),and minimal error clustering.Parametric analysis reveals significant effects of parameters,length and location of source(B),(D),heat generation/absorption coefficient(Q),and porosity parameter(ε).Increasing the cooling area length(B)reduces streamline intensity and local Nusselt and Sherwood numbers,while decreasing isotherms,isoconcentrations,and micro-rotation.The Bejan number(Be+)decreases with increasing(B),whereas(Be+++),and global entropy(e+++)increase.Variations in(Q)slightly affect streamlines but reduce isotherm intensity and average Nusselt numbers.Higher(D)significantly impacts isotherms,iso-concentrations,andmicro-rotation,altering streamline contours and local Bejan number distribution.Increased(ε)enhances streamline strength and local Nusselt number profiles but has mixed effects on average Nusselt numbers.These findings highlight the complex interactions between cooling area length,fluid flow,and heat transfer properties.By combining finite volume method(FVM)with machine learning technique,this study provides valuable insights into the complex interactions between key parameters and heat transfer,contributing to the development of more efficient designs in applications such as cooling systems,energy storage,and bioengineering.
文摘文章研究探讨了人工神经网络(ANN)在网络推荐算法领域的运用。ANN算法具备将用户和物品的特征向量映射至低维空间的能力,通过衡量用户过往行为与潜在推荐对象间的相似程度,迅速锁定用户可能青睐的内容,进而实现个性化推荐。研究过程中,我们运用ANN方法对用户的浏览、收藏及加购行为的权值展开了动态解析与计算。重点关注于多层人工神经网络模型,购买模型和基于距离的向量匹配算法相结合,并在模型中提出高效且准确的个性化推荐系统。提出感知器模型,进而提出多层人工神经网络,进行网络预测,不仅解决了信息茧房与过度推荐的问题,还解决了用户反馈难以进行个性化推荐的问题,并以此提出一种基于距离的向量匹配算法,针对不同用户推送个性化的商品。实验阶段,我们依据用户对商品的收藏频次、购买相关商品的次数以及浏览该商品及其相关商品的数量,来预测用户即将购买的商品数量及其在各商品上的浏览时长。将网络预测的结果与实际测试集数据进行比对后,我们发现网络预测与实际情况展现出较高的吻合度。随后采用基于距离的向量匹配技术,针对不同用户推送个性化的商品信息。最后进行对比分析,与其他网络预测方法比较,突出本文方法的优势。In this paper, We studies the application of ANN (artificial neural network) method in network recommendation algorithm. ANN algorithm can map the feature vectors of users and objects into the low dimensional space, by calculating the similarity between user historical behavior and candidate recommendation items, quickly find the content that the user may be interested in, and realize personalized recommendation.In the process of research, we used the ANN method to dynamically analyse and calculate the weights of users’ browsing, collection and purchase behaviours. This paper focuses on the combination of multi-layer artificial neural network model, purchase model and distance-based vector matching algorithm, and proposes an efficient and accurate personalised recommendation system in the model. The perceptron model is proposed, and then a multi-layer artificial neural network is proposed for network prediction, which not only solves the problem of information cocoon and over-recommendation, but also solves the problem that it is difficult to make personalised recommendations based on user feedback, and proposes a distance-based vector matching algorithm to push personalised products for different users. At the same time, making personalized recommendations using ANN methods can gain new cognition, create new wisdom, and produce more valuable decisions. Explore the consumer needs of the users from the historical data, explore the new cognition from the old data, create the new wisdom from the new cognition combined with the machine learning algorithms, and finally help the users to find the goods they are interested in, and present the most suitable products to the users. Finally, a comparative analysis is conducted to highlight the advantages of our method compared to other network prediction methods.
