Optimization of heterogeneous Catalyst-assisted fatty acid methyl esters biodiesel production from Soybean oil with different Machine learning methods

There is a growing attention to the bio and renewable energies due to fast depletion of fossil fuels as well as the global warming problem. Here, we developed a modeling and simulation method by means of artificial intelligence (AI) for prediction of the bioenergy production from vegetable bean oil. AI methods are well known for prediction of complex and nonlinear process. Three distinct Adaptive Boosted models including Huber regression, LASSO, and Support Vector Regression (SVR) as well as artificial neural network (ANN) were applied in this study to predict actual yield of Fatty acid methyl esters (FAME) production. All boosted utilizing the Adaptive boosting algorithm. The important influencing parameters on the biodiesel production such as the catalyst loading (CAO/Ag, wt%) and methanol to oil (Soybean oil) molar ratio were selected as the input variables of models while the yield of FAME production was selected as output. Model hyper-parameters were tuned to maintain generality while improving prediction accuracy. The models were evaluated using three distinct metrics Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R². Error rates of 8.16780E-01, 4.43895E-01, 2.06692E + 00, and 3.92713 E-01 were obtained with the MAE metric for boosted Huber, SVR, LASSO and ANN models. On the other hand, the RMSE error of these models were about 1.092E-02, 1.015E-02, 2.669E-02, and 1.01174E-02, respectively. Finally, the R-square score were calculated for boosted Huber, boosted SVR, and boosted LASSO as 0.976, 0.990, 0.872, and 0.99702, respectively. Therefore, it can be concluded that although the boosted SVR and ANN models were better models for prediction of process efficiency in terms of error, but all algorithms had high accuracy. The optimum yield of 83.77% and 81.60% for biodiesel production were observed at optimum operating values from boosted SVR and ANN models, respectively.     

Well-posedness for the massive nonlinear wave equation on asymptotically AdS spacetimes

In this paper, we prove some well-posedness theorems for the nonlinear massive wave equation (with the mass satisfying the Breitenlohner-Freedman bound) on asymptotically anti-de Sitter spaces. The solution is constructed by contraction mapping method and also as a limit of solutions to an initial boundary value problem with boundary at a finite location in spacetime by finally pushing the boundary out to infinity. The solution obtained is unique within the energy class.     

Experimental and theoretical assessment of Fe-doped indium-oxide-based dilute magnetic semiconductors

Specimens of iron-doped indium oxide (In_1-xFe_x)₂O₃ with x?=?0.015, 0.03, 0.045 and 0.06, amalgamated through a traditional solid-state reaction method followed by H₂/air sintering, were characterised using an X-ray diffractometer (XRD), a vibrating sample magnetometer (VSM), and a scanning electron microscope (SEM) to investigate their structural, magnetic and morphological properties respectively. According to XRD plots, all the specimens exhibit cubic bixbyite structures along with ancillary phases. Magnetic assessment showed that In₂O₃ has a negative susceptibility, exhibiting diamagnetic behaviour at room temperature. The doping of Fe ions induces ferromagnetic (FM) ordering, which is enhanced with increasing doping content. The strength of the magnetisation increases when the specimens are exposed to H₂ but is reduced on further air sintering. A bound magnetic polaron (BMP) model is successfully fitted to the observed FM data involving localised carriers and magnetic cations. A multivariate assessment viz. a hierarchical cluster analysis (HCA) was used to corroborate and strengthen the experimental determined magnetic properties. A homogeneous particle distribution was observed in all SEM micrographs and is validated through MATLAB-based simulation by applying a watershed segmentation algorithm. Surface plots also confirm the change in magnetic properties with increase in doping concentration.     

柠檬果茶中游离态和键合态挥发性成分分析

以柠檬果茶为研究对象,建立了顶空固相微萃取前处理结合气相色谱质谱联用技术测定其中挥发性化合物的分析方法。采用开水冲泡对样品进行提取,通过Amberlite XAD-2大孔吸附树脂对柠檬果茶中的糖苷类挥发性组分键合,分离游离态和键合态化合物,甲醇溶剂作为洗脱剂对键合态化合物进行洗脱,Almondsβ-D-葡萄糖苷酶对其酶解。使用气质联用对样品中游离态和键合态挥发性成分进行检测,其结果根据数据库匹配和对比文献保留时间定性,内标法进行定量。结果表明,柠檬果茶中含有游离态物质24种,键合态物质16种,主要为(+)-柠檬烯、1-辛醇、橙花醇、(-)-4-萜品醇、alpha-松油醇等。方法为花果茶干燥工艺提供参考。     

The Bertini irreducibility theorem for higher codimensional slices

In [3], Poonen and Slavov recently developed a novel approach to Bertini irreducibility theorems over an arbitrary field, based on random hyperplane slicing. In this paper, we extend their work by proving an analogous bound for the dimension of the exceptional locus in the setting of linear subspaces of higher codimensions.     

一带一路背景下企业地域影响力发展的规划研究

面对一个分布在新地理区域的新市场,合理规划区域服务影响力的发展战略对企业至关重要。本文假设企业的服务影响力在地理区域上的发展可以用其地理上分布的立足点来表示。单个立足点覆盖有限范围内的地理区域,并且在一个特定时刻用所有立足点所覆盖地理区域的总和来表示企业该时刻的发展状态。由于资源限制,企业对经济发展迅速的区域的覆盖必须在一定时间内逐步完成。因此,在发展时期内如何确定立足点的位置并使其位于知名城市中,是企业管理者亟待解决的问题。针对这个问题,本文首先基于时间序列预测方法建立了区域经济指标评估模型;然后使用集合覆盖理论提出了以成本和满意度为目标的选址优化模型并使用分支定界算法求解该模型;最后,使用从“一带一路”地区收集的数据对模型进行了检验。结果表明,该方法对有相关问题的企业具有较大的参考价值。