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.     

琅玡山抽水蓄能电站厂房位置及轴线方向的优化选择

地下厂房位置及轴线方向的选择是一项重要的地质工作,本文在分析琅山抽水蓄能电站厂房区工程地质条件的基础上,就厂房位置及其轴线方向的优化选择,进行了分析和研究。     

A step-by-step guide to building two-population stochastic mortality models

Two-population stochastic mortality models play a crucial role in the securitization of longevity risk. In particular, they allow us to quantify the population basis risk when longevity hedges are built from broad-based mortality indexes. In this paper, we propose and illustrate a systematic process for constructing a two-population mortality model for a pair of populations. The process encompasses four steps, namely (1) determining the conditions for biological reasonableness, (2) identifying an appropriate base model specification, (3) choosing a suitable time-series process and correlation structure for projecting period and/or cohort effects into the future, and (4) model evaluation.For each of the seven single-population models from Cairns et al. (2009), we propose two-population generalizations. We derive criteria required to avoid long-term divergence problems and the likelihood functions for estimating the models. We also explain how the parameter estimates are found, and how the models are systematically simplified to optimize the fit based on the Bayes Information Criterion. Throughout the paper, the results and methodology are illustrated using real data from two pairs of populations.     

Mean-Variance Hedging Under Multiple Defaults Risk

We solve a mean-variance hedging problem in an incomplete market where multiple defaults can occur. For this purpose, we use a default-density modeling approach. The global market information is formulated as a progressive enlargement of a default-free Brownian filtration, and the dependence of the default times is modelled using a conditional density hypothesis. We prove the quadratic form of each value process between consecutive default times and recursively solve systems of coupled quadratic backward stochastic differential equations (BSDEs). We demonstrate the existence of these solutions using BSDE techniques. Then, using a verification theorem, we prove that the solutions of each subcontrol problem are related to the solution of our global mean-variance hedging problem. As a byproduct, we obtain an explicit formula for the optimal trading strategy. Finally, we illustrate our results for certain specific cases and for a multiple defaults case in particular.     

论地质灾害防治工程的地质观与工程观

概括前人的经验, 结合作者个人的实践, 本文较详细地讨论了地质灾害防治工程的地质观念与工程观念。提出地质观的核心问题是地质灾害体的变形破坏力学机理分析, 工程观观与工程观运用的最佳耦合。文中还讨论了超前预测的作用, 监测反馈与工程效果检验的关系, 以及地质工程师与设计工程师的关系等, 最后以链子崖危岩体防治工程为例进行了说明。     

基于iPLS的血清胆固醇、甘油三酯近红外定量分析

为了建立血清样品胆固醇、甘油三酯近红外分析最优模型,利用近红外透射光谱技术结合间隔偏最小二乘法（iPLS）建立预测模型。结果表明,胆固醇最优建模波段是1700—1798nm,最优预测模型的相关系数Rp、预测均方差RMSEP分别为0.984、0.198mmol/L;甘油三酯最优建模波段是1654-1746nm,最优预测模型的Rp、RMSEP分别为0.967、0.157mmol/L。采用iPLS建立血清胆固醇、甘油三酯定量分析模型,不仅可以提高模型的预测精度,而且模型更加简洁、数据运算量也更少,优选出的特征谱区还可为设计小型专用近红外分析仪器提供依据。     

Performance of two-hop infrastructure based multi-antenna regenerative relaying in Rayleigh fading channel

Cooperative relaying is considered as an effective technique to enlarge the coverage area and enhance the system capacity for the future wireless systems. In this paper, an infrastructure based multi-antenna cooperative relay network has been investigated. Closed form expressions of outage probability and average error rate have been derived, when the relay and the destination perform selection combining of the signals. The relay is assumed to operate in the adaptive decode and forward mode. The effect of number of antennas installed on the relay and their placement has also been studied.     

Application of plug–plug technique to ACE experiments for discovery of peptides binding to a larger target protein: A model study of calmodulin‐binding fragments selected from a digested mixture of reduced BSA

To discover peptide ligands that bind to a target protein with a higher molecular mass, a concise screening methodology has been established, by applying a “plug–plug” technique to ACE experiments. Exploratory experiments using three mixed peptides, mastoparan‐X, β‐endorphin, and oxytocin, as candidates for calmodulin‐binding ligands, revealed that the technique not only reduces the consumption of the protein sample, but also increases the flexibility of the experimental conditions, by allowing the use of MS detection in the ACE experiments. With the plug–plug technique, the ACE–MS screening methodology successfully selected calmodulin‐binding peptides from a random library with diverse constituents, such as protease digests of BSA. Three peptides with K_d values between 8–147 μM for calmodulin were obtained from a Glu‐C endoprotease digest of reduced BSA, although the digest showed more than 70 peaks in its ACE–MS electropherogram. The method established here will be quite useful for the screening of peptide ligands, which have only low affinities due to their flexible chain structures but could potentially provide primary information for designing inhibitors against the target protein.     

Eppstein's bound on intersecting triangles revisited

Let S be a set of n points in the plane, and let T be a set of m triangles with vertices in S. Then there exists a point in the plane contained in Ω(m³/(n⁶log²n)) triangles of T. Eppstein [D. Eppstein, Improved bounds for intersecting triangles and halving planes, J. Combin. Theory Ser. A 62 (1993) 176-182] gave a proof of this claim, but there is a problem with his proof. Here we provide a correct proof by slightly modifying Eppstein's argument.     

<Emphasis Type="Italic">L</Emphasis><Superscript>2</Superscript>-approximating Pricing under Restricted Information

We consider the mean-variance hedging problem under partial information in the case where the flow of observable events does not contain the full information on the underlying asset price process. We introduce a certain type martingale equation and characterize the optimal strategy in terms of the solution of this equation. We give relations between this equation and backward stochastic differential equations for the value process of the problem. This work was supported by Georgian National Science Foundation grant STO07/3-172.