CALIBRATION OF BONDING MODEL PARAMETERS FOR COATED FERTILIZERS BASED ON PSO-BP NEURAL NETWORK

In this paper, the ultimate crushing displacement and ultimate crushing load of the coated fertilizer granules were obtained by uniaxial compression test as 0.450 mm and 58.668 N, respectively. Then the DEM model of the encapsulated fertilizer was established, and the Plackett-Burman and Steepest ascent tests were taken to determine the factors that had significant effects on the results and their ranges of values, respectively. Finally, the PSO_BP neural network was trained using full-factor test data, and the correlation coefficients of training process, validation process, testing process and overall performance were obtained as 0.98057, 0.95781, 0.96724 and 0.97459, respectively, indicating that the trained PSO_BP neural network fits well and can predict the ultimate crushing displacement and ultimate crushing load. The ultimate crushing displacement Y1 and ultimate crushing load Y2 are 0.450 mm and 58.703 N, with a minimum relative error of 0.06% from the actual value. This study can provide new methods and ideas for the calibration of discrete element simulation parameters.

Optimization of S-Nitrosocaptopril Monohydrate Storage Conditions Based on Response Surface Method

From unstable crystals to relatively stable monohydrate crystals, many researchers have been working on S-nitrosocaptopril for more than two decades. S-nitrosocaptopril monohydrate (Cap-NO·H2O) is a novel crystal form of S-nitrosocaptopril (Cap-NO), and is not only a nitric oxide (NO) donor, but also an angiotensin-converting enzyme inhibitor (ACEI). Yet, a method for long-term storage has never been reported. In order to determine the optimal storage conditions, Plackett–Burman (PB) design was performed to confirm the critical factors. Response surface methodology (RSM) was employed to determine the optimal Cap-NO·H2O storage condition, based on the rough interval determined by the path of steepest ascent experiment. The optimized storage condition was denoted as nitrogen purity of 97%, temperature of −10 °C and 1.20 g deoxidizer. In this case, a final preservation rate of 97.91 ± 0.59% could be obtained. In specific storage conditions, Cap-NO·H2O was found to be stable for at least 6 months in individual PE package, procreating a potentially applicable avenue.

Simultaneous Improvement of Lincomycin a Production and Reduction of Lincomycin B Levels in Streptomyces Lincolnensis Using a Combined Medium Optimization Approach

PurposeThe current study aimed to optimize the culture and production parameters of industrial production of lincomycin A by Streptomyces lincolnensis using a statistical approach that could also reduce unwanted by-products. MethodsThe Plackett-Burman design, steepest ascent method, and response surface design were used to evaluate different factors that affect lincomycin A production. ResultsUsing an optimized S. lincolnensis fermentation medium, lincomycin A production was increased up to 4600 mg/L in shaking flasks, which indicated a 28.3% improvement over previous production in an un-optimized medium (3585 mg/L). Additionally, the concentration of lincomycin B by-product was reduced to 0.8%, which was 82.2% lower than that in the un-optimized medium. Further, quantitative real-time PCR analysis revealed the optimized medium improved lincomycin A production by stimulating key genes in the lincomycin A biosynthesis pathway, as well as an osmotic stress gene. ConclusionsOptimizing the fermentation medium improved lincomycin A production and decreased that of the lincomycin B by-product, which could help cut production costs and simplify downstream separation processes. We found that osmotic stress reduced the concentration of lincomycin B, which could also help reduce fermentation by-product yields in other actinobacteria.

Explaining Deep Neural Network Models with Adversarial Gradient Integration

Deep neural networks (DNNs) have became one of the most high performing tools in a broad range of machine learning areas. However, the multilayer non-linearity of the network architectures prevent us from gaining a better understanding of the models’ predictions. Gradient based attribution methods (e.g., Integrated Gradient (IG)) that decipher input features’ contribution to the prediction task have been shown to be highly effective yet requiring a reference input as the anchor for explaining model’s output. The performance of DNN model interpretation can be quite inconsistent with regard to the choice of references. Here we propose an Adversarial Gradient Integration (AGI) method that integrates the gradients from adversarial examples to the target example along the curve of steepest ascent to calculate the resulting contributions from all input features. Our method doesn’t rely on the choice of references, hence can avoid the ambiguity and inconsistency sourced from the reference selection. We demonstrate the performance of our AGI method and compare with competing methods in explaining image classification results. Code is available from https://github.com/pd90506/AGI.

Pencarian Rute Optimal Dengan Metode Steepest Ascent Hill Climbing Untuk Tempat Wisata Di Bandung Menggunakan Android

The lack of information on the path to tourist attractions makes tourists confused in choosing the shortest path. To overcome this, this study proposes an optimal route search system with the steepest ascent hill climbing method to find the most optimal route to tourist attractions. The search process for this tourist spot begins by looking for the latitude and longitude distance of each tourist spot along with the distance between tourist attractions using google maps, then the distance is entered into the steepest ascent hill climbing method with the initial stage of determining the number of combinations, where the combination results in the number of derivative trails. Then swap the initial path with the derivative path using graph theory. This process is repeated so that if the initial trajectory is smaller than the descended path, the effective route and route will be displayed on google maps. So based on the system testing conducted, the steepest ascent hill climbing method used as the main method of this research can be implemented in the search for the most effective or fastest route using the undirected graph method for the exchange process between tourist attractions

Integrated Utilization of Dairy Whey in Probiotic β-Galactosidase Production and Enzymatic Synthesis of Galacto-Oligosaccharides

This work established an integrated utilization of dairy whey in β-galactosidase production from Lactobacillus bulgaricus and prebiotics synthesis by the probiotic enzyme. A cost-effective whey-based medium was newly developed for culturing Lactobacillus bulgaricus to produce β-galactosidase. The medium was optimized through response surface methodology (RSM) involving a series of statistical designs, such as the Plackett–Burman design, steepest ascent experiment, and central composite design. Under the optimized medium, the β-galactosidase activity of L. bulgaricus reached 2034 U/L, which was twice that produced from the traditional MRS medium. The cells of L. bulgaricus harvested from the whey-based medium were subsequently treated with lysozyme. The resulting crude enzyme was used as an efficient catalyst, which catalyzed the synthesis of the prebiotic galacto-oligosaccharides (GOS) in a high yield of 44.7% by using whey (200 g/L) as the substrate. The sugar mixture was further purified by activated charcoal adsorption, thereby yielding a high-purity level of 77.6% GOS.

Procedure to find out the optimal ranges of process parameters for friction stir welding

The present study deals with the application of sequential procedure (i.e. steepest ascent) to obtain the optimum values of process parameters for conducting friction stir welding (FSW) experiments. A vertical milling machine is modified by fabricating fixture and tool ( H13 material) for performing FSW operation to join AA7039 plates. The steepest ascent technique is employed to design the experiments at different rotational speed, welding speed, and tilt angle. The ultimate tensile strength is considered as a performance characteristic for deciding the optimal levels. The mechanical and metallurgical characteristics of the joints are studied by executing tensile and microhardness tests. It is concluded from the graphical analysis of the steepest ascent technique that the optimal maximum and minimum values are 1812–1325 r/min for rotational speed, 43–26 mm/min for welding speed, and 2°–1.3° for tilt angle, respectively. Besides, optical microscope and scanning electron microscope are utilized for microstructural and fractographic analyses for a better understanding of the process.