AbstractFeature selection targets for selecting relevant and useful features, and is a vital challenge in turbulence modeling by machine learning methods. In this paper, a new posterior feature selection method based on validation dataset is proposed, which is an efficient and universal method for complex systems including turbulence. Different from the priori feature importance ranking of the filter method and the exhaustive search for feature subset of the wrapper method, the proposed method ranks the features according to the model performance on the validation dataset, and generates the feature subsets in the order of feature importance. Using the features from the proposed method, a black-box model is built by artificial neural network (ANN) to reproduce the behavior of Spalart-Allmaras (S-A) turbulence model for high Reynolds number (Re) airfoil flows in aeronautical engineering. The results show that compared with the model without feature selection, the generalization ability of the model after feature selection is significantly improved. To some extent, it is also demonstrated that although the feature importance can be reflected by the model parameters during the training process, artificial feature selection is still very necessary.
By using pythagorean fuzzy sets and T-S fuzzy descriptor systems, the new (α, β)-pythagorean fuzzy descriptor systems are proposed in this paper. Their definition is given firstly, and the stability of this kind of systems is studied, the relation of (α, β)-pythagorean fuzzy descriptor systems and T-S fuzzy descriptor systems is discussed. The (α, β)-pythagorean fuzzy controller and the stability of (α, β)-pythagorean fuzzy descriptor systems are deeply researched. The (α, β)-pythagorean fuzzy descriptor systems can be better used to solve the problems of actual nonlinear control. The (α, β)-pythagorean fuzzy descriptor systems will be a new research direction, and will become a universal method to solve practical problems. Finally, an example is given to illustrate effectiveness of the proposed method.
Treatment with pyrazine derivatives—antituberculosis pyrazinamide (PZA), anticancer bortezomib (BZM), and antifungal pyrazine-2-amidoxime (PAOX) and pyrazine-2-thiocarboxamide (PTCA)—is associated with side effects, as observed in the case of other therapeutic drugs. To prevent the side effects of pyrazine derivatives, researchers are working to develop a universal method that will detect these compounds in body fluids. There is a lack of literature data about voltammetric measurements with poly-L-amino acid-modified GCEs surfaces. The available reports describe the application of various modifications of these electrodes for the detection of different active substances of drugs; however, they do not indicate one particular method for the detection of drugs with a pyrazine skeleton. This research aimed to prepare three types of glassy carbon electrodes (GCEs) with modified surfaces by electropolymerization using 1, 10, and 100 mM solutions of L-glycine (Gly), L-alanine (Ala), L-lysine (Lys), respectively. The poly-amino acid coatings applied on GCE surfaces were analyzed in detail under a three-dimensional (3D) microscope and were used as chemosensors of four pyrazine drugs in stoichiometric tests. The results were compared with the measurements made on an unmodified GCE. To obtain reliable results, the linearity of measurements was also verified in the concentration gradient and appropriate scanning speed was chosen to achieve the most accurate measurements.
AbstractThe recent detection of potent carcinogenic nitrosamine impurities in several human medicines has triggered product recalls and interrupted the supply of critical medications for hundreds of millions of patients, illuminating the need for increased testing of nitrosamines in pharmaceutical products. However, the development of analytical methods for nitrosamine detection is challenging due to high sensitivity requirements, complex matrices, and the large number and variety of samples requiring testing. Herein, we report an analytical method for the analysis of a common nitrosamine, N-nitrosodimethylamine (NDMA), in pharmaceutical products using full evaporation static headspace gas chromatography with nitrogen phosphorous detection (FE-SHSGC-NPD). This method is sensitive, specific, accurate, and precise and has the potential to serve as a universal method for testing all semi-volatile nitrosamines across different drug products. Through elimination of the detrimental headspace-liquid partition, a quantitation limit of 0.25 ppb is achieved for NDMA, a significant improvement upon traditional LC-MS methods. The extraction of nitrosamines directly from solid sample not only simplifies the sample preparation procedure but also enables the method to be used for different products as is or with minor modifications, as demonstrated by the analysis of NDMA in 10+ pharmaceutical products. The in situ nitrosation that is commonly observed in GC methods for nitrosamine analysis was completely inhibited by the addition of a small volume solvent containing pyrogallol, phosphoric acid, and isopropanol. Employing simple procedures and low-cost instrumentation, this method can be implemented in any analytical laboratory for routine nitrosamine analysis, ensuring patient safety and uninterrupted supply of critical medications.
A facial, versatile, and universal method that breaks the substrate limits is desirable for antifouling treatment. Thin films of functional poly-p-xylylenes (PPX) that are deposited using chemical vapor deposition (CVD) provide a powerful platform for surface immobilization of molecules. In this study, we prepared an alkyne-functionalized PPX coating on which poly (sulfobetaine methacrylate-co-Az) could be conjugated via click chemistry. We found that the conjugated polymers were very stable and inhibited cell adhesion and protein adsorption effectively. The same conjugation strategy could also be applied to conjugate azide-containing poly (ethylene glycol) and poly (NIPAAm). The results indicate that our method provides a simple and robust tool for fabricating antifouling surfaces on a wide range of substrates using CVD technology of functionalized poly (p-xylylenes) for biosensor, diagnostics, immunoassay, and other biomaterial applications.
The article presents a universal method for determining the professional suitability (PS) of a candidate and an algorithm for forming a psychological profile for a specific profession based on determining the psychological potential of personality. The developed method is based on the use of automated support systems. Based on the obtained value of the integral indicator, a decision is made on the PS group of this candidate. This method adapts to the requirements of the profession to candidates, taking into account changes in the conditions of activity by adjusting the typical psychological profile of the personality. The developed method for determining a candidate’s PS has been brought to practical implementation in the form of an Automated Psychodiagnostic Complex (APDC) “Psychodiagnostics.” APDC has been tested on the example of the psychological selection procedures of personnel for military service in units with law enforcement functions. APDС allows to reduce the time and labor costs for conducting psychodiagnostic studies, increases the reliability of tests due to a higher degree of standardization of the testing procedure, increases the accuracy of assessing psychological characteristics, and reduces the likelihood of errors in the processing of test results. APDС can be used for recruiting in various sectors of the economy, education, and military sphere.
Cerebral organoids are three-dimensional cell-culture systems that represent a unique experimental model reconstructing early events of human neurogenesis in vitro in health and various pathologies. The most commonly used approach to studying the morphological parameters of organoids is immunohistochemical analysis; therefore, the three-dimensional cytoarchitecture of organoids, such as neural networks or asymmetric internal organization, is difficult to reconstruct using routine approaches. Immunohistochemical analysis of biological objects is a universal method in biological research. One of the key stages of this method is the production of cryo- or paraffin serial sections of samples, which is a very laborious and time-consuming process. In addition, slices represent only a tiny part of the object under study; three-dimensional reconstruction from the obtained serial images is an extremely complex process and often requires expensive special programs for image processing. Unfortunately, staining and microscopic examination of samples are difficult due to their low permeability and a high level of autofluorescence. Tissue cleaning technologies combined with Light-Sheet microscopy allows these challenges to be overcome. CLARITY is one of the tissue preparation techniques that makes it possible to obtain opaque biological objects transparent while maintaining the integrity of their internal structures. This method is based on a special sample preparation, during which lipids are removed from cells and replaced with hydrogel compounds such as acrylamide, while proteins and nucleic acids remain intact. CLARITY provides researchers with a unique opportunity to study three-dimensional biological structures while preserving their internal organization, including whole animals or embryos, individual organs and artificially grown organoids, in particular cerebral organoids. This protocol summarizes an optimization of CLARITY conditions for human brain organoids and the preparation of Light-Sheet microscopy samples.
The impact of a wheeled agricultural vehicle on the deformable support surface determines the vehicle's ability to move, as well as soil compaction, which is not desirable in agriculture. The agricultural machine must not cause more pressure on the ground than is permissible. Therefore, in the tasks of design numerical modelling of the agricultural vehicle movement or trailer, it is required to calculate the specified parameter. It is impossible to calculate without knowledge of the geometric characteristics of the contact spot associated with the normal deformation of the tire under normal load. To calculate these characteristics, it is necessary to have universal dependencies for determining the normal stiffness of the tire. These are available for tires of various purposes. The elastic properties of ultra-low pressure tires are insufficiently studied. Experimental studies of the elastic properties of these tires have been carried out with the authors participation. However, there are currently no dependencies to describe them. This does not provide the possibility of a correct design calculation of the influence of such tires on the soil. The purpose of the work: to develop a universal method for calculating the influence on the soil of agricultural vehicle. A universal method for calculating the impact of a wheeled agricultural machine on the ground has been developed. Universal design-experimental dependence for determining the normal stiffness of ultra-low pressure tires is obtained. It takes into account tire pressure, normal load under specific conditions and geometric characteristics.
Glucosinolates (GSLs) are important precursor compounds with anticancer activities in Brassicaceae vegetables and are readily hydrolyzed by myrosinase. Given the diversity of these species, establishing an accurate and universal method to quantify intact GSLs in different plant tissues is necessary. Here, we compared and optimized three tissue disruption methods for sample preparation. After microwave treatment for 90 s, 13 GSLs in homogenized Chinese cabbage samples were recovered at 73–124%. However, a limitation of this method was that different tissues could not be processed under the same microwave conditions. Regarding universality, GSLs in Brassicaceae vegetables could be extracted from freeze-dried sample powder with 70% methanol (v/v) or frozen-fresh sample powder with 80% methanol (v/v). Moreover, heating extraction is necessary for GSLs extracted from frozen-fresh sample powder. Average recoveries of the two optimized methods were 74–119% with relative standard deviations ≤15%, with the limits of quantification 5.72–17.40 nmol/g dry weight and 0.80–1.43 nmol/g fresh weight, respectively. Notably, the method for analyzing intact GSLs was more efficient than that for desulfo-GSLs regarding operational complexity, detection speed and quantification accuracy. The developed method was applied to identify the characteristic GSLs in 15 Brassicaceae vegetables, providing a foundation for further research on GSLs.