Solubility Optimization of Loxoprofen as a Nonsteroidal Anti-Inflammatory Drug: Statistical Modeling and Optimization

Industrial-based application of supercritical CO₂ (SCCO₂) has emerged as a promising technology in numerous scientific fields due to offering brilliant advantages, such as simplicity of application, eco-friendliness, and high performance. Loxoprofen sodium (chemical formula C₁₅H₁₈O₃) is known as an efficient nonsteroidal anti-inflammatory drug (NSAID), which has been long propounded as an effective alleviator for various painful disorders like musculoskeletal conditions. Although experimental research plays an important role in obtaining drug solubility in SCCO₂, the emergence of operational disadvantages such as high cost and long-time process duration has motivated the researchers to develop mathematical models based on artificial intelligence (AI) to predict this important parameter. Three distinct models have been used on the data in this work, all of which were based on decision trees: K-nearest neighbors (KNN), NU support vector machine (NU-SVR), and Gaussian process regression (GPR). The data set has two input characteristics, P (pressure) and T (temperature), and a single output, Y = solubility. After implementing and fine-tuning to the hyperparameters of these ensemble models, their performance has been evaluated using a variety of measures. The R-squared scores of all three models are greater than 0.9, however, the RMSE error rates are 1.879 × 10⁻⁴, 7.814 × 10⁻⁵, and 1.664 × 10⁻⁴ for the KNN, NU-SVR, and GPR models, respectively. MAE metrics of 1.116 × 10⁻⁴, 6.197 × 10⁻⁵, and 8.777 × 10⁻⁵errors were also discovered for the KNN, NU-SVR, and GPR models, respectively. A study was also carried out to determine the best quantity of solubility, which can be referred to as the (x₁ = 40.0, x₂ = 338.0, Y = 1.27 × 10⁻³) vector.     

基于贝叶斯参数优化的无模型自适应硅单晶直径控制

直拉硅单晶的生长过程涉及多场多相耦合与复杂的物理化学变化,其中工艺参数的波动是导致晶体直径不均匀的重要原因,如何实现工艺参数的控制以获得理想的、均匀的晶体直径具有重要的研究意义。本文分析现有控制方法存在不稳定以及控制效果不佳的问题后,提出基于贝叶斯参数优化的无模型自适应控制模型来控制硅单晶生长过程中的晶体直径。首先以坩埚上升速度与加热器的功率作为控制输入参数,晶体直径作为输出,搭建无模型自适应控制模型,并分析算法的稳定性。其次将控制模型进行仿真实验,发现硅单晶直径控制模型中不同的超参数设定会影响控制过程的迭代次数以及控制效果。最后,利用贝叶斯优化超参数的取值范围,并进行最终的仿真实验,结果表明,经贝叶斯参数优化后的控制模型计算快、迭代次数少,输出的晶体直径稳定,同时将生长工艺参数控制在实际生产要求范围内。因此,基于贝叶斯参数优化的无模型自适应控制实现了硅单晶直径均匀稳定的有效控制,具有结合工程背景的实际应用前景。     

Antibacterial Activity of a Novel Oligosaccharide from Streptomyces californics against Erwinia carotovora subsp. Carotovora

The present study aims to characterize and predict models for antibacterial activity of a novel oligosaccharide from Streptomyces californics against Erwinia carotovora subsp. carotovora using an adaptive neuro-fuzzy inference system and an artificial neural network. The mathematical predication models were used to determine the optimal conditions to produce oligosaccharide and determine the relationship between the factors (pH, temperature, and time). The characteristics of the purified antibacterial agent were determined using ultraviolet spectroscopy (UV/Vis), infrared spectroscopy (FT-IR), nuclear magnetic resonance spectroscopy (¹H- and ¹³C-NMR), and mass spectrometry (MS). The best performances for the model were 39.45 and 35.16 recorded at epoch 1 for E. carotovora Erw₅ and E. carotovora EMCC 1687, respectively. The coefficient (R²) of the training was more than 0.90. The highest antimicrobial production was recorded after 9 days at 25 °C and a pH of 6.2, at which more than 17 mm of the inhibition zone was obtained. The mass spectrum of antimicrobial agent (peak at R.T. = 3.433 of fraction 6) recorded two molecular ion peaks at m/z = 703.70 and m/z = 338.30, corresponding to molecular weights of 703.70 and 338.30 g/mol, respectively. The two molecular ion peaks matched well with the molecular formulas C₂₉H₅₃NO₁₈ and C₁₄H₂₆O₉, respectively, which were obtained from the elemental analysis result. A novel oligosaccharide from Streptomyces californics with potential activity against E. carotovora EMCC 1687 and E. carotovora Erw₅ was successfully isolated, purified, and characterized.     

An Experimental Design Approach to Quantitative Expression for Quality Control of a Multicomponent Antidiabetic Formulation by the HILIC Method

A rapid and reproducible hydrophilic liquid chromatography (HILIC) process was established for concomitant determination of remogliflozin etabonate (RE), vildagliptin (VD), and metformin (MF) in a formulation. A face-centered central composite experimental design was employed to optimize and predict the chromatographic condition by statistically studying the surface response model and design space with desirability close to one. A HILIC column with a simple mobile phase of acetonitrile (65% v/v) and 20 mM phosphate buffer (35% v/v, pH 6, controlled with orthophosphoric acid) was used to separate RE, VD, and MF. RE, VD, and MF were separated in 3.6 min using an isocratic mode mobile phase flow at a flow rate of 1.4 mL at room temperature, and the analytes were examined by recording the absorption at 210 nm. The developed HILIC method was thoroughly validated for all parameters recommended by ICH, and linearity was observed in the ranges 20–150 µg/mL, 10–75 µg/mL, and 50–750 µg/mL for RE, VD, and MF, respectively, along with excellent regression coefficients (r² > 0.999). The calculated percentage relative deviation and relative error ascertained the precision and accuracy of the method. The selectivity and accuracy were further confirmed by the high percentage recovery of added standard drugs to the formulation using the standard addition technique. The robustness of the HILIC processes was confirmed by developing a half-normal probability plot and Pareto chart, as the slight variation of a single factor had no significant influence on the assay outcomes. Utilization of the optimized HILIC procedure for concurrent quantification of RE, VD, and MF in solid dosage forms showed accurate and reproducible results. Hence, the fast HILIC method can be regularly employed for the quality assurance of pharmaceutical preparations comprising RE, VD, and MF.     

Geometric and operational optimization of 20-kHz probe-type sonoreactor for enhancing sonochemical activity

The use of a 20-kHz probe-type sonicator irradiating downward in a 500 mL vessel was optimized for the enhancement of the sonochemical activity in terms of the geometric and operational factors. These factors included the probe immersion depth (the vertical position of the probe), input power, height of the liquid from the bottom, horizontal position of the probe, and thickness of bottom plate The sonochemical oxidation reactions were investigated both quantitatively and qualitatively using calorimetry, KI dosimetry, and luminol (Sonochemiluminescence, SCL) techniques. The sonochemical activity was very positively affected by the vertical boundaries. The highest sonochemical activity was obtained when the probe was placed close to the bottom of the vessel (immersion depth of 60 mm), with a high input power (input power of 75%), and optimal liquid height condition (liquid height of 70 mm). The SCL image analysis showed that the cavitational activity zone gradually expanded around the probe body and changed into a circular shape as the experimental conditions were optimized, and consequently the sonochemical activity increased. The formation of a large bright circular-shaped activity zone could be attributed to the strong reflections of the ultrasound firstly, at the vessel bottom and secondly, at the liquid surface. On the other hand, the cavitational activity zone and the sonochemical activity were negatively affected by the horizontal boundaries when the probe was placed close to the side wall of the vessel. In addition, it was found that the sonochemical activity was also significantly affected by the thickness of the support plate owing to the reflection and transmission of the ultrasound at the boundary between the liquid and the solid media.     

An analytical approach to global optimization

Global optimization problems with a few variables and constraints arise in numerous applications but are seldom solved exactly. Most often only a local optimum is found, or if a global optimum is detected no proof is provided that it is one. We study here the extent to which such global optimization problems can be solved exactly using analytical methods. To this effect, we propose a series of tests, similar to those of combinatorial optimization, organized in a branch-and-bound framework. The first complete solution of two difficult test problems illustrates the efficiency of the resulting algorithm. Computational experience with the programbagop, which uses the computer algebra systemmacsyma, is reported on. Many test problems from the compendiums of Hock and Schittkowski and others sources have been solved.The research of the first and the third authors has been supported by AFOSR grants #0271 and #0066 to Rutgers University. Research of the second author has been supported by NSERC grant #GP0036426 and FCAR grants #89EQ4144 and #90NC0305.     

Finding the Minimal Root of an Equation with the Multiextremal and Nondifferentiable Left-Hand Part

A problem very often arising in applications is presented: finding the minimal root of an equation with the objective function being multiextremal and nondifferentiable. Applications from the field of electronic measurements are given. Three methods based on global optimization ideas are introduced for solving this problem. The first one uses an a priori estimate of the global Lipschitz constant. The second method adaptively estimates the global Lipschitz constant. The third algorithm adaptively estimates local Lipschitz constants during the search. All the methods either find the minimal root or determine the global minimizers (in the case when the equation under consideration has no roots). Sufficient convergence conditions of the new methods to the desired solution are established. Numerical results including wide experiments with test functions, stability study, and a real-life applied problem are also presented.     

Efficient Algorithms for Large Scale Global Optimization: Lennard-Jones Clusters

A stochastic global optimization method is applied to the challenging problem of finding the minimum energy conformation of a cluster of identical atoms interacting through the Lennard-Jones potential. The method proposed incorporates within an already existing and quite successful method, monotonic basin hopping, a two-phase local search procedure which is capable of significantly enlarging the basin of attraction of the global optimum. The experiments reported confirm the considerable advantages of this approach, in particular for all those cases which are considered in the literature as the most challenging ones, namely 75, 98, 102 atoms. While being capable of discovering all putative global optima in the range considered, the method proposed improves by more than two orders of magnitude the speed and the percentage of success in finding the global optima of clusters of 75, 98, 102 atoms.     

A hybrid genetic algorithm–neural network strategy for simulation optimization

Simulation optimization aims at determining the best values of input parameters, while the analytical objective function and constraints are not explicitly known in terms of design variables and their values only can be estimated by complicated analysis or time-consuming simulation. In this paper, a hybrid genetic algorithm–neural network strategy (GA–NN) is proposed for such kind of optimization problems. The good approximation performance of neural network (NN) and the effective and robust evolutionary searching ability of genetic algorithm (GA) are applied in hybrid sense, where NNs are employed in predicting the objective value, and GA is adopted in searching optimal designs based on the predicted fitness values. Numerical simulation results and comparisons based on a well-known pressure vessel design problem demonstrate the feasibility and effectiveness of the framework, and much better results are achieved than some existed literature results.     

Stability analysis of the Pareto optimal solutions for some vector boolean optimization problem

In this article we consider the boolean optimization problem of finding the set of Pareto optimal solutions. The vector objectives are the positive cuts of linear functions to the non-negative semi-axis. Initial data are subject to perturbations, measured by the l ₁-norm in the parameter space of the problem. We present the formula expressing the extreme level (stability radius) of such perturbations, for which a particular solution remains Pareto optimal.