COMPARISON OF THREE EXTRACTION METHODS OF ALLOPURINOL IN URIC ACID HERBAL MEDICINE WITH HIGH-PERFORMANCE LIQUID CHROMATOGRAPHY QUANTIFICATION

Objective: This study was aimed to understand and determine the effectiveness of allopurinol extraction in herbal medicine from three extraction methods based on parameters of accuracy and precision. Methods: The study consisted of three methods including dissolving and filtering, liquid-liquid extraction, and solid-phase extraction with mixed-mode cation exchanger (SPE-MCX). The procedures were carried out using NaOH and HCl in dissolving and filtering method; methanol, HCl, and ethyl acetate in liquid-liquid extraction; and NH4OH elution solvent in SPE-MCX. Results: The results showed that extraction effectiveness based on accuracy level was the dissolving and filtering method>SPE-MCX>liquid-liquid extraction with % recovery+SD of 91.314+2.903%, 87.533+4.950%, and 54.549+3.517%, respectively. The precision level was the dissolution and filtering method>SPE-MCX>liquid-liquid extraction based on % relative standard deviations (RSD) of 3.18%, 5.226%, and 6.446%, respectively. Conclusion: It can be concluded that the allopurinol extraction method with the highest effectiveness based on accuracy and precision parameters in herbal medicine is the dissolving and filtering method.

COVID-19 Chest X-Ray Detection Performance Through Variations of Wavelets Basis Function

Our previous work regarding the X-Ray detection of COVID-19 using Haar wavelet feature extraction and the Support Vector Machines (SVM) classification machine has shown that the combination of the two methods can detect COVID-19 well but then the question arises whether the Haar wavelet is the best wavelet method. So that in this study we conducted experiments on several wavelet methods such as biorthogonal, coiflet, Daubechies, haar, and symlets for chest X-Ray feature extraction with the same dataset. The results of the feature extraction are then classified using SVM and measure the quality of the classification model with parameters of accuracy, error rate, recall, specification, and precision. The results showed that the Daubechies wavelet gave the best performance for all classification quality parameters. The Daubechies wavelet transformation gave 95.47% accuracy, 4.53% error rate, 98.75% recall, 92.19% specificity, and 93.45% precision.

Real-world EUROCRINE® registry data challenge the reliability of Bethesda cytopathology for thyroid surgery indication

Objectives Fine-needle aspiration cytology (FNAC) is recommended by international guidelines for the preoperative evaluation of suspicious thyroid nodules >1 cm. Despite robust evidence from endocrine centers demonstrating the key role of FNAC results for the indication of surgery, the method is not routinely used in European clinics. The database EUROCRINE®, which was introduced in 2015 with the scope of registering operations of the endocrine system, allows for a large-scale analysis of the current service reality in Europe concerning FNAC use and associated accuracy. Methods Operations performed to “exclude malignancy”, registered from January 2015 to December 2018 in EUROCRINE®, were analyzed. Parameters of accuracy were calculated for FNAC. FNAC results were considered “test positive” in the case of Bethesda category IV, V, and VI, since these categories usually prompt surgical interventions in European centers for thyroid surgery. Bethesda category II and III were considered “test negative”. Results Of 8,791 operations, 5,780 had preoperative FNAC (65.7%). The overall malignancy rate was 28.3% (2,488/8,791). Malignancy rates were 68.8% for Bethesda VI, 69.9% for Bethesda V, 32.6% for Bethesda IV, 28.2% for III, 20.2% for Bethesda II, and 24.5% for Bethesda I. After exclusion of papillary microcarcinomas (PTMCs), the sensitivity of FNAC was 71.7% and specificity 43.5%, the positive predictive value was 29.1% and the negative predictive value 82.7%. Conclusions Although the indication to “exclude malignancy” was the predominant reason that prompted thyroid resection in the present cohort, FNAC was only used in about 65.7% of cases. When performed, FNAC was associated with unexpectedly low accuracy. Interestingly, in Bethesda II, 20.2% of malignant entities were present (13.3% after the exclusion of PTMCs).

On the Sensitivity of the Gamma of the Quantum Cryptographic System AKM2017 to Changes in the Session Key

The quantum cryptographic system AKM2017 is considered. The results of the analysis of the dependence of the degree of difference between the encryption and decryption gamut on the degree of difference between the corresponding session keys are presented. The equality of these degrees of distinction is revealed and substantiated. For an arbitrarily fixed encryption session key, the distribution of session decryption keys by classes is revealed and described, depending on the value of the degree of difference between the encryption and decryption gamuts. One class is made up of all session decryption keys, leading to the same value of the degree of difference between the encryption and decryption gamuts. A geometric interpretation of the specified distribution by classes is given in the form of placement along circles (class is a circle) on the surface of a sphere of unit radius centered at the origin of the Euclidean rectangular coordinate system in a three-dimensional linear space over the field of real numbers. The stated results can be used to solve the problems of optimizing the values of the parameters of accuracy and reliability of the functioning of variants of practical implementations of the quantum cryptographic system AKM2017, for example, when setting up a session decryption key, which makes it possible to guarantee a predetermined small value of the mathematical expectation of the number of incorrectly decrypted binary plain text.

Rapid Quantification of the Palm Kernel Biokerosene Content in Mixtures with Aviation Kerosene using MIR Spectroscopy and Multivariate Regression by PLS

An alternative methodology was developed to monitor the biokerosene content of palm kernel in blend with kerosene using medium infrared spectroscopy associated with partial least squares (PLS). The efficiency of this methodology was analyzed based on the parameters of accuracy and figures of merit. The values of root-mean-square error of cross-validation (RMSECV), root-mean-square error of calibration (RMSEC) and root-mean-square error of prediction (RMSEP) were in agreement because the RMSEP was higher than RMSECV and RMSEC. In addition, the RMSEP value is considered acceptable according to the Brazilian standard ABNT NBR 15568 because it is less than 1%. The figures of merit were performed in agreement with the requirements established in the standard ASTM E1655-05. The linearity of the model was assessed based on the analysis of the model fit through the correlation of the actual and predicted values of the calibration and prediction sets, where a high correlation between the values was evidenced, with a correlation coefficient (R) exceeding 0.99. The good results of the application of MIR spectroscopy combined with multivariate regression by PLS suggest that this analytical methodology is feasible, efficient and suitable for use by inspection agencies to control the biokerosene content of palm kernel in mixture with diesel.

Technological prerequisites for the energy efficiency increase of the engine cylinder bore processing

An example of the rationalization of the current technology for manufacturing a cylinder of a small-sized diesel engine at PA “TULAMASHZAVOD” from a cast iron is given. It is shown that lowpower obsolete equipment leads to an increase in the number of technological operations and equipment used. However, even if all the generally accepted technological recommendations are followed, only 80% of the parts meet the requirements for the required parameters of accuracy and quality of the cylinder bore surface. Studies have found that the main reasons for insufficient quality are the presence of residual stresses in the casting, which cannot be removed during artificial aging, as well as insufficient accuracy and rigidity of metal cutting equipment at turning and boring operations. The use of more powerful and accurate modern equipment is theoretically and experimentally justified, which, due to increased refinement, allows reducing the number of machining operations. Replacement of artificial thermal aging by natural, combined with the replacement of shaft furnaces for heat treatment of workpieces with chamber furnaces of lower power and higher capacity, reduce energy consumption for the manufacturing by almost 4 times.

Classifying the Biological Status of Honeybee Workers Using Gas Sensors

Honeybee workers have a specific smell depending on the age of workers and the biological status of the colony. Laboratory tests were carried out at the Department of Apiculture at UWM Olsztyn, using gas sensors installed in two twin prototype multi-sensor detectors. The study aimed to compare the responses of sensors to the odor of old worker bees (3–6 weeks old), young ones (0–1 days old), and those from long-term queenless colonies. From the experimental colonies, 10 samples of 100 workers were taken for each group and placed successively in the research chambers for the duration of the study. Old workers came from outer nest combs, young workers from hatching out brood in an incubator, and laying worker bees from long-term queenless colonies from brood combs (with laying worker bee’s eggs, humped brood, and drones). Each probe was measured for 10 min, and then immediately for another 10 min ambient air was given to regenerate sensors. The results were analyzed using 10 different classifiers. Research has shown that the devices can distinguish between the biological status of bees. The effectiveness of distinguishing between classes, determined by the parameters of accuracy balanced and true positive rate, of 0.763 and 0.742 in the case of the best euclidean.1nn classifier, may be satisfactory in the context of practical beekeeping. Depending on the environment accompanying the tested objects (a type of insert in the test chamber), the introduction of other classifiers as well as baseline correction methods may be considered, while the selection of the appropriate classifier for the task may be of great importance for the effectiveness of the classification.

Perbandingan Performansi Kinerja Algoritma Pengklasifikasian Terpandu Untuk Kasus Penyakit Kardiovaskular

One of the health problems that occur in Indonesia is the increasing number of NCD (Non-Communicable Disease) such as heart attack and cardiovascular disease. There are two factors that cause cardiovascular disease, i.e. factor that can be changed and cannot be changed. This study aim to analyze the best performance of several classification algorithms such as k-nearest neighbors algorithm (k-NN), stochastic gradient descent (SGD), random forest (RF), neural network (NN) and logistic regression (LR) in classifying cardiovascular based on factors that caused those diseases. There are two aspects that need to be examined, the performance of each algorithm which is evaluated using the Confusion matrix method with the parameters of accuracy, precision, recall and AUC (Area Under the Curve). The dataset uses 425.195 samples from result data of cardiovascular disease diagnosed. The testing mode uses percentage split and cross-validation technique. The experimental results show that the performance of NN algorithms produces the best prediction accuracy compared to other algorithms, which is accuracy of 89.60%, AUC of 0.873, precision of 0.877, and recall of 0.896 using percentage split and cross-validation testing mode using Orange. For the accuracy of 89.46%, AUC of 0.865, precision of 0.875, and recall of 0.895 using cross-validation testing mode using Weka. By KNIME, the result of accuracy value is 88.55%, AUC value is 0.768, precision value is 0.854, and recall value is 0.886 using cross-validation testing mode.

Calculation of Deviations of Shape, Size and Location of Flat Surfaces

The development of computerization allows the development of mathematical models of physical processes with different methods of blade machining of machine parts. Modern programmable CNC machines have the ability to produce parts in an automated cycle, ensuring the requirements of the drawing. Control of the quality of processing of the main flat surfaces, which are technological and Assembly bases, should be considered in more detail. Their accuracy in accordance with the standards is determined by the shape deviations from the adjacent planes, size and relative position. The standards have instructions on how to create a virtual adjacent plane using adjustable supports and further scan the ordinates of its individual points to calculate the deviation of the form. To quantify the deviations of shape, size and location is not enough to have a field f, the vectors of which are defined from the geometric plane, given the nominal size, it is necessary to establish a base for their reference. The question of establishing bases for reference is not only of fundamental importance, since different bases can be obtained different values of deviations. The paper proposes a mathematical model for determining the named parameters of accuracy from the middle planes, which are determined by the calculated vectors at specific points of the treated surface when processing batches of blanks on milling machines face milling.