Confidence Probability Research Articles

Identification of mineralogical ore varieties using ultrasonic measurement results

Purpose. To improve the measurement and information base of ultrasonic measurements rock characteristics to assess their mineralogical varieties. It is proposed to use a combination of measurement results of the acoustic quality factor of the test sample in relation to longitudinal and transverse ultrasonic waves, as well as the characteristic coefficient based on the dispersion and the average amplitude value of the received signal, for fuzzy identification of mineralogical and technological varieties of iron ore. Methods. As elastic waves propagate through the rock mass, they undergo attenuation due to absorption and dissipation of ultrasonic signal energy. The degree of attenuation, as well as the wave propagation velocity, is dependent on the physical-mechanical and chemical-mineralogical properties of the medium through which they travel. In this paper, we analyze a rock characterized by a complex structure comprising ore inclusions and surrounding matrix, each of which differs in its physical-mechanical and chemical-mineralogical properties. In particular, in iron ore samples, the distribution of mineral grains and aggregates exhibits significant heterogeneity in terms of both amount and size. Findings. An iterative method of fuzzy identification of mineralogical-technological iron ore varieties, based on the analysis of their properties in vector space of features, allows, by minimizing the sums of weighted distances between the analyzed and reference values of ultrasonic measurement results, to attribute them with a certain degree of belonging to the main technological types of ores mined at the deposit, and define them as magnetite quartzite with a confidence probability of 0.93. Originality. As an information base for identification of mineralogical iron ore varieties, the results of measuring the velocity and attenuation of longitudinal and transverse ultrasonic waves of appropriate frequency are used, on the basis of which the acoustic quality factor of the rock sample is calculated, as well as the characteristic parameter S, which is determined by the dispersion and average values of the received ultrasonic signal intensity, which has traveled a certain distance in the studied environment. Practical implications. The results of tests and practical approbation of the method for identifying mineralogical iron ore varieties based on the data of ultrasonic well logging testify to its high efficiency, which allows recommending the developed scientific-technical solutions for wide industrial application at mining enterprises.

Probabilistic Power and Energy Balance Risk Scheduling Method Based on Distributed Robust Optimization

The volatility and uncertainty associated with the high proportion of wind and PV output in the new power system significantly impact the power and energy balance, making it challenging to accurately assess the risks related to renewable energy abandonment and supply guarantee. Therefore, a probabilistic power and energy balance risk analysis method based on distributed robust optimization is proposed. Firstly, the affine factor and the flexible ramp reserve capacity of thermal power are combined to establish a probabilistic index, which serves to characterize the risk associated with the power and energy balance. Drawing upon the principles of the conditional value at risk theory, the risk indexes of the load shedding power and curtailment power under a certain confidence probability are proposed. Secondly, the probability distribution fuzzy sets of uncertain variables are constructed using the distributionally robust method to measure the Wasserstein distance between different probability distributions. Finally, aiming at minimizing the operation cost of thermal power, the risk cost of power abandonment, and the risk cost of load shedding, a distributed robust optimal scheduling model based on a flexible ramp reserve of thermal power is established.

Error is a number, parameter or probability distribution

The problem of the format (the form of representation by difference, scattering parameter, distribution) of the characteristics of the accuracy of the results in solving measurement problems in terms of the theory of errors and the concept of uncertainty is considered. It is shown that for the error as a difference, the key question is the uncertainty of the so-called true value. For measurement uncertainty, when switching to estimating the scattering parameter based on a set of repeated measurements and getting rid of the adjective true, a number of key issues of a different plan arise – the lack of logic of statistical inference, the inconsistency of the term “confidence level” with the term “confidence probability” in state verification schemes and the practical uselessness of measurement uncertainty characteristics in risk analysis tasks. However, the endless improvement of measurement methods and tools in the theory of errors and an unlimited amount of information for a complete description of the measured value in the concept of uncertainty is a common defect of both approaches, only in other words. Conceptually, the transition from error to uncertainty is an intermediate stage to the representation of accuracy characteristics by probability distributions. A brief overview of the transformation of the point of view of international metrology on this issue is given. The evaluation of the accuracy of the results is presented as a structural-parametric identification of the drift of metrological characteristics of measuring instruments and standards, as well as metrological certification of methods for solving measurement problems in the cross-observation scheme in terms of probability distributions. This allows you to get rid of the problems of incompleteness of other formats for representing accuracy characteristics. The most complete characterization of accuracy for the theory of errors and the concept of uncertainty is the format of the probability distribution. However, the endless improvement of measurement methods and tools in the theory of errors and an unlimited amount of information for a complete description of the measured value in the concept of uncertainty is a common defect of both approaches, only in other words. Conceptually, the transition from error to uncertainty is an intermediate stage to the representation of accuracy characteristics by probability distributions. A brief overview of the transformation of the point of view of international metrology on this issue is given. The evaluation of the accuracy of the results is presented as a structural-parametric identification of the drift of metrological characteristics of measuring instruments and standards, as well as metrological certification of methods for solving measurement problems in the cross-observation scheme in terms of probability distributions. This allows you to get rid of the problems of incompleteness of other formats for representing accuracy characteristics. The most complete characteristic of accuracy for the theory of errors and the concept of uncertainty is the format of the probability distribution.

A headspace injection double-column dual-detector gas chromatography system for the analysis of 12 volatile compounds such as ethanol in human blood

Based on the technical methods of GB/T 42430-2023 and GA/T 204-2019, this study established an analytical method for headspace injection double-column dual-detector (hydrogen flame ion detector) gas chromatography for the simultaneous analysis of at least 12 volatile compounds, including ethanol, in human blood using two different equipment platforms and chromatographic columns. A 100 μL blood or urine sample and a 0.04 g/L tert-butanol working solution prepared as an internal standard are introduced into the headspace sample bottle and then sealed, mixed, and placed on the headspace sampler rack. Using different equipment platforms and columns, methodological parameters such as the limit of detection (LOD), limit of quantification (LOQ), precision, and accuracy of the method were systematically evaluated. The chromatographic separation of acetone, alcohols and benzenes using the established method was satisfactory. The linear ranges, linear correlation coefficients (r), and LODs of acetone and six alcohols, including ethanol, were 0.10-3.00 g/L, >0.997, and 0.05 g/L, respectively. The LOQs were 0.10 g/L for all other compounds, excluding n-propanol (0.005 g/L). Additionally, the linear ranges, r values, LODs, and LOQs of benzene and four benzene derivatives were 0.05-50 mg/L, >0.995, 0.02 mg/L, and 0.05 mg/L, respectively (Column J&W DB-BAC1 UI and Column Rtx-BAC-PLUS 2). The average recoveries of compounds on J&W DB-BAC1 UI and Rtx-BAC-PLUS 2 columns ranged from 92.2% to 111.6%, and the relative standard deviations (RSDs, n=6) ranged from 0.4% to 7.4%. The LOD, LOQ, precision, accuracy, and linearity of the established method met the requirements of relevant standards, and no significant differences arose between the methodological parameters of the two platforms. CNAS-GL006 (2019) and JJF 1059.1-2012 were used as guides to evaluate the uncertainty of ethanol on two different sets of equipment platforms and chromatographic columns. The ethanol uncertainty was mainly derived from the calibration curve; however, the confidence probability was 95% (k=2). According to the analysis of the verification samples and real samples, the established method is suitable for the high-precision quantitative analysis of acetone and six alcohols and five benzene derivatives in human blood and other body fluids. It can be used in practical scenarios such as judicial identification and the detection of poisons.

Clinical manifestations of scarlet fever in children in the megalopolis at the present stage.

BACKGROUND: according to the literature, an increase in severe forms of scarlet fever among children was noted in a number of foreign countries in 2022-2023. AIMS: to identify the current clinical features of scarlet fever in children in a large metropolis of Russia. MATERIALS AND METHODS: an observational descriptive retrospective continuous study of the clinical features of the course of scarlet fever in children was conducted according to 295 case histories of patients diagnosed with Scarlet fever from 2020 to 2022 on the basis of the GBUZ "DGKB named after Z.A. Bashlyaeva DZM", GBUZ "DGKB No. 9 named after G.N. Speransky DZM" in Moscow. The statistical processing of the material uses traditional methods for calculating extensive and intensive indicators (p1 and p2), errors of extensive indicators (m1 and m2) and standard deviations (rmax↔rmip), taking into account the confidence interval (t) and confidence probability (P=95%), a method for comparing indicators using a well-known formula, where at t ≥ 2.0 P ≥ 95% p ≤ 0.05, the definition of standard deviations, taking into account the confidence interval (t) and the confidence probability P = 95%. The comparison of percentages in the analysis of four-field conjugacy tables was performed using the exact Fisher criterion. The differences were considered statistically significant at p 0.05. RESULTS: as a result of the study, it was revealed that the abolition of strict anti-epidemic restrictions on COVID-19 in 2022 led not only to an increase in the incidence of scarlet fever among children from 0 to 18 years of age by 3.6 times in 2022 compared with 2021, but, as a result, to an increase in hospitalization of children in hospitals in Moscow. Thus, the number of hospitalized children with scarlet fever in 2022 increased by 18.0 times compared to 2020. The majority of those hospitalized (83.4%) were children from organized groups, including those aged 3 to 7 years (52.9%) and from 7 to 14 years (39.3%), with an average severity of the disease (93.6%). It was noted that that among the examined children, 53 children (18%) had a history of COVID-19, and among this group of children, one in five children had severe scarlet fever, while among children who did not have a history of COVID-19, these were significantly (p 0.001) 8.4 times less (respectively, 20.7% versus 2.5%). It was revealed that every 8-9 patients (12.2%) had various complications, mainly of a septic nature and those that arose in the first week of the disease, including otitis media, sinusitis, community-acquired pneumonia, parathonsillar and lateral pharyngeal abscesses and toxic shock syndrome. In the second week and later, complications of an infectious and allergic nature were reported, including tubulointerstitial nephritis, acute renal injury, post-infectious cardiomyopathy, acute post-streptococcal glomerulonephritis, toxic allergic vasculitis, post-streptococcal arthritis. Beta-lactam antibiotics remain the etiotropic drugs of choice. Antibiotics of the cephalosporin 53.6% and penicillin 46.4% series were used to treat scarlet fever. CONCLUSIONS: in modern conditions of the megalopolis, scarlet fever occurs with a typical clinical picture. The majority of those hospitalized were children of preschool and school age, only 12.2% of children were diagnosed with various complications of scarlet fever and the vast majority of children (93.6%) had a moderate severity of scarlet fever. It was noted that against the background of an increase in the number of hospitalized patients, the number of patients with severe scarlet fever decreases, while the incidence of complications in patients does not change (12.3% - 12.8%). It was shown that among patients who had suffered from COVID-19, scarlet fever was 8.3 times more likely to occur in severe form compared with children who had no history of COVID-19 disease (respectively, 20.7% vs. 2.5, p 0.0001).

Categorizing Dermatological Malignancies Via Computational Methods

In this study, a machine learning model is developed to classify different types of cancer using convolutional neural networks (CNNs) for image processing. The core objective is to achieve a performance level comparable to that of dermatologists. The model is trained on a substantial dataset of medical images, enabling it to learn and recognize various characteristics indicative of different cancer types. By leveraging the power of CNNs, the model can process these images effectively, identifying subtle patterns and features that are often challenging to detect with the naked eye. The training process involves feeding the CNN with labelled images, enabling it to differentiate between benign and malignant cases with high accuracy. Through rigorous testing, the model demonstrates competence on par with experienced dermatologists, both in terms of sensitivity and specificity. This equivalence in performance is particularly significant as it underscores the model's potential to aid in clinical settings, providing reliable second opinions and enhancing diagnostic workflows. A user interface is also developed to allow input images to be analysed by the trained CNN model. This interface not only displays the model’s predictions but also provides essential metrics such as confidence scores and probability distributions. These metrics offer valuable insights into the model's decision-making process, aiding clinicians in understanding and trusting the AI's assessments. Overall, the findings suggest that convolutional neural networks hold substantial promise for improving cancer diagnosis. The model's high performance in classification tasks demonstrates its viability as a tool for supporting dermatologists in clinical practice. By reducing diagnostic errors and accelerating the identification process, this technology has the potential to significantly impact patient outcomes and advance the field of medical imaging and diagnostics. Keywords: Convolutional Neural Networks (CNNs); Cancer Classification; Medical Image Processing; Dermatology AI; Diagnostic Accuracy

Large-Scale Land Cover Mapping Framework Based on Prior Product Label Generation: A Case Study of Cambodia

Large-Scale land cover mapping (LLCM) based on deep learning models necessitates a substantial number of high-precision sample datasets. However, the limited availability of such datasets poses challenges in regularly updating land cover products. A commonly referenced method involves utilizing prior products (PPs) as labels to achieve up-to-date land cover mapping. Nonetheless, the accuracy of PPs at the regional level remains uncertain, and the Remote Sensing Image (RSI) corresponding to the product is not publicly accessible. Consequently, the sample dataset constructed through geographic location matching may lack precision. Errors in such datasets are not only due to inherent product discrepancies, and can also arise from temporal and scale disparities between the RSI and PPs. In order to solve the above problems, this paper proposes an LLCM framework for generating labels for use with PPs. The framework consists of three main parts. First, initial generation of labels, in which the collected PPs are integrated based on D-S evidence theory and initial labels are obtained using the generated trust map. Second, for dynamic label correction, a two-stage training method based on initial labels is adopted. The correction model is pretrained in the first stage, then the confidence probability (CP) correction module of the dynamic threshold value and NDVI correction module are introduced in the second stage. The initial labels are iteratively corrected while the model is trained using the joint correction loss, with the corrected labels obtained after training. Finally, the classification model is trained using the corrected labels. Using the proposed land cover mapping framework, this study used PPs to produce a 10 m spatial resolution land cover map of Cambodia in 2020. The overall accuracy of the land cover map was 91.68% and the Kappa value was 0.8808. Based on these results, the proposed mapping framework can effectively use PPs to update medium-resolution large-scale land cover datasets, and provides a powerful solution for label acquisition in LLCM projects.

Measuring decision confidence

We examine whether the way individuals randomize between options captures their decision confidence. In two experiments in which subjects faced pairs of options (a lottery and a varying sure payment), we allowed subjects to choose randomization probabilities according to which they would receive each option. Separately, we obtained two measures of self-reported confidence - confidence statements and probabilistic confidence - for choosing between the two options. Consistent with the predictions of two theoretical frameworks incorporating preference uncertainty, the randomization probabilities correlated strongly with both self-reported measures (median Spearman correlations between 0.86 to 0.89) and corresponded in absolute levels to probabilistic confidence. This relationship is robust to two exogenous manipulations of decision confidence, where we varied the complexity of the lottery and subjects’ experience with the lottery.

Экспериментальное определение внешних характеристик тяговых подстанций переменного тока для выбора параметров активных устройств системы тягового электроснабжения

Objective: to develop a method of experimental determination of external characteristics of alternating current traction substations for selection of parameters of active devices of traction power supply system. Methods: finding of equivalent currents of phases of a step-down transformer using the theory of linear electric circuits; statistical methods, including: construction and approximation of external characteristics of regression analysis methods, construction of theoretical and experimental law of distribution of equivalent current of traction load and determination of current quantile for a given confidence probability. Results: advantages and disadvantages of methods of simulation modeling of traction power supply system as applied to determination of parameters of active devices are shown, possibilities of experimental method are given. A detailed description of the conducted experiment is given, connection schemes of measuring devices and vector diagrams of currents and voltages are shown. The main stages of the proposed methodology for selecting the parameters of the booster transformer are outlined. Calculation formulas for determining the equivalent phase currents are presented, the external characteristic for the selected traction substation is constructed. The theoretical and experimental distribution law of the equivalent current of the traction load was constructed and the quantiles of the equivalent current were determined, which amounted to 400 A at a confidence level of 0.968. The maximum required value of volt additive for the selected traction substation and the law of volt additive voltage control for the booster transformer were determined. Practical importance: the proposed methodology of selecting the parameters of active devices of traction power supply system based on the experimental determination of external characteristics of traction substations will allow to determine more accurately the energy performance of booster transformers. It is supposed to improve the methodology by processing the results of synchronous measurements of currents and voltages at neighboring traction substations feeding the inter-substation zone. This will make it possible to expand the possibilities of application of active devices of the traction power supply system and improve the algorithms of booster transformer operation, including taking into account the operation of controlled reactive power compensation devices.

Correlation of ctDNA and LDH levels in patients with soft tissue sarcoma: A single-center experience.

e23535 Background: Our study aims to examine the relationship between the presence of circulating tumor DNA (ctDNA) and lactate dehydrogenase (LDH) in patients diagnosed with soft tissue sarcoma (STS). There is emerging yet limited data on the applicability of ctDNA in STS. LDH is a cheaper readily available test that has been associated with unfavorable overall survival in different malignancies. This research aims to understand the correlation between ctDNA and LDH as a tool to assess its utility as indirect markers of response to treatment in STS. Methods: This was a single-center retrospective chart review on patients diagnosed with STS. Eligibility criteria included a confirmed diagnosis and available measurements of both ctDNA and LDH levels. Correlation was compared between Non-hispanic whites (NHW) and Hispanics (H). Data was sourced from our electronic medical record and Signatera™ assay to obtain the primary endpoints. Specimens were standardized in accordance to Natera’s™ specifications. Study endpoints included the correlation coefficients between ctDNA and LDH levels. A two-stage study design was applied for a robust analysis. Spearman's rank correlation coefficient (denoted as r) was employed for statistical analysis, assessing the strength and direction of the monotonic relationship between ctDNA and LDH levels. Marker values were treated as continuous variables in these analyses. All analyses were executed in GraphPad Prism software. Results: The cohort comprised 33 patients with STS (14 NHW, 19 H). The median LDH in H was 189 (interquartile range (IQR) = 184) vs 184 (IQR = 105) in NHW. Comparatively, the median ctDNA in H was 0 (IQR = 39.3) and also 0 (IQR = 11.1) in NHW. The overall correlation between ctDNA and LDH levels was moderate positive (r = 0.5, p = 0.002), indicating a consistent, though not necessarily linear, relationship. Subgroup analysis showed r = 0.19 (p = 0.52) in NHW and r = 0.43 (p = 0.1) in H groups, suggesting ethnic variability in the ctDNA-LDH correlation. These findings were statistically significant within the predefined confidence limits and probability values. Conclusions: Our study demonstrated a significant correlation between ctDNA levels and LDH in STS patients, implying that higher ctDNA may be associated with higher LDH levels. This association can lead clinicians in monitoring LDH more frequently as an indirect marker of response to treatment in STS. This correlation suggests an increased tumor burden with a higher inflammatory response, with variations across different racial groups. The wider availability of LDH testing compared to ctDNA in many centers highlights LDH's potential as an indirect marker for STS. Further research with larger prospective cohorts is essential to confirm LDH's role paired with ctDNA in guiding treatment decisions in STS, enhancing its utility in clinical practice.

Adaptive Robust Control of Uncertain Euler-Lagrange Systems Using Gaussian Processes.

This article proposes a novel adaptive robust control approach based on Gaussian processes (GPs) for the high-precision tracking problem of uncertain Euler-Lagrange (EL) systems with time-varying external disturbances. Given a prior dynamic model, the GP regression (GPR) technique is employed to obtain a nonparametric data-based uncertainty model, including its probabilistic confidence intervals. Based on the adaptive sliding mode control (ASMC) framework, the posterior means of GPs are utilized for dynamic compensation, whereas the posterior variances are applied to adjust the feedback gains. This proposed control strategy is robust against significant system uncertainty with low feedback gains. A novel adaptive law for updating hyperparameters based on tracking error feedback is presented, thereby improving the performance of both tracking control and GP modeling simultaneously. Compared to existing likelihood-based optimization methods, this hyperparameter adaptive law enables data-efficient and fast uncertainty learning for control applications. The proposed control strategy guarantees the semiglobal asymptotic convergence to zero tracking error with a specified probability. Simulations using an underwater robot model demonstrate that the utilization of GPs and hyperparameter adaptive law significantly improves the performance of tracking control and uncertainty learning.

Research on the Analysis Method of Measured Pressure Data during the Transient Process of Load Rejection in Hydropower Stations

Abstract This paper proposes a method to analyze the measured pressure data in the transition process of hydropower station. The method first separates the measured pressure data to the mean pressure and pressure fluctuation data, then divides the pressure fluctuation data into different time domain grids, and performs the confidence probability processing on the pressure fluctuation data in each time grid, and finally obtains the credible measured pressure data of transient process. In this paper, the specific implementation steps of the method are introduced by taking the results of a load rejection test of a pumped storage power station as an example.

THE METHOD OF QUANTITATIVE DETERMINATION OF THE AMOUNT OF FLAVONOIDS IN THE FLOWERS OF GIANT CEPHALARIA

Giant cephalaria (Cephalaria gigantea (LEDEB.) BOBROV) is a powerful perennial whose height reaches two meters. This plant is found in Southern Europe, Western and Central Asia, as well as in North and South Africa. It is cultivated in the Botanical garden of Samara University. The extracts of giant cephalaria have been used in traditional medicine for many years due to their antimicrobial, antifungal, cytotoxic, antioxidant, antidiabetic and antipyretic activities, which may be due to various biologically active compounds. It is known that the leaves contain triterpenoids, phenolic carboxylic acids and their derivatives, flavonoids, and the flowers contain flavonoid compounds: luteolin, quercetin, cynaroside, quercimeritrin and gigantoside A. The article describes the development of a technique for quantifying of the amount of flavonoids in the flowers of giant cephalaria. Spectrophotometric analysis of water-alcohol extracts from giant cephalaria flowers allowed us to establish that the main contribution to the absorption curve of their UV-spectra in the presence of AlCl3 is made by flavonols having a free OH group at the C-3 position, and in the differential version, the maximum absorption of the tested solution is close to that of the standard sample of quercetin (428±2 nm). Optimal conditions for the extraction of flavonoids in giant cephalaria flowers were determined: extractant 70% ethyl alcohol; the ratio "raw material-extractant" – 1 : 50; extraction time – extraction in a boiling water bath for 60 minutes, the degree of grinding of raw materials – 2 mm, analytical wavelength – 426 nm. It was determined that the content of the total flavonoids calculated on quercetin in the flowers of giant cephalaria varies from 1.58±0.05% to 2.63±0.05%. The error of a single determination with a 95% confidence probability is ±1.75%. The obtained results were used in the development of the FS project for a new type of medicinal plant raw materials "Cephalaria giant flowers" for introduction into the State Pharmacopoeia of the Russian Federation.

Comparative Analysis of the Performance of Artificial Neural Networks in Assessing the Technical Condition of Steel Ropes

Introduction. Currently, artificial neural networks (ANN) are successfully used for technical diagnostics of steel ropes. Expensive software products with an adapted neural network implementation environment, such as STATISTICA, Amygdala, MatLab Simulink, are often used for this purpose. The most affordable way to build and train an ANN, from a financial point of view, is to write your own program code using interactive libraries such as TensorFlow, PyTorch, Scikit-learn. However, such libraries are not fully adapted for building an ANN, and to use them you need to have basic programming skills. As a result, the quality of an ANN depends not only on its architecture, training data, and composition, but also on the environment in which it is built. The aim of the work was to compare the quality of the ANN, built and trained by various methods according to the criterion of test network performance, confidence levels for assessing the technical condition of the rope, as well as the complexity and speed of training. For this purpose, new software has been developed to solve the problem of assessing the technical condition of a steel rope using a combination of various rejection indicators. Materials and Methods. The basis for an ANN training was a statistical database of typical damages of steel ropes and, an expert assessment of the technical condition of steel ropes. The software was written in the Python programming language. Various methods of programming a neural network were presented: an ANN built on the basis of the STATISTICA software package and an ANN built using the interactive Scikit-learn library. Ten test samples were prepared to verify the operation of the ANN. The ANN quality was assessed based on the test network performance and confidence probabilities (activation levels of the “winning” neuron) of determining the technical condition of the rope. Results. The construction of the ANN using the interactive library Scikit-learn showed a relatively high complexity of construction and a relatively low learning rate of the ANN. Test performance of the network, with a test sample size of ten, turned out to be the same for both built ANNs. At the same time, there was a difference in the indicator of the average confidence level for determining the technical condition of a steel rope between the results of the ANN built on the basis of the STATISTICA software package and the ANN built using the Scikit-learn interactive library. Discussion and Conclusion. The results showed that the ANN built using the STATISTICA software package with the same architecture and network learning parameters had more optimal software algorithms according to the criteria of confidence probability and network learning speed in comparison with the ANN built using the free Skicit-learn library. However, the indicator of the ANN test performance turned out to be the same for both ANNs. This result justified the use of TensorFlow, PyTorch, and Skicit-learn libraries by the world's leading research and commercial centers in the field of artificial intelligence. The obtained scientific result allows us to numerically evaluate and compare the quality of an ANN having the same architecture and learning parameters, but built using different methods. This will be useful for future scientific research in the field and for selecting the optimal environment for constructing ANNs in industrial applications.

Principal Uncertainty Quantification With Spatial Correlation for Image Restoration Problems.

Uncertainty quantification for inverse problems in imaging has drawn much attention lately. Existing approaches towards this task define uncertainty regions based on probable values per pixel, while ignoring spatial correlations within the image, resulting in an exaggerated volume of uncertainty. In this paper, we propose PUQ (Principal Uncertainty Quantification) - a novel definition and corresponding analysis of uncertainty regions that takes into account spatial relationships within the image, thus providing reduced volume regions. Using recent advancements in generative models, we derive uncertainty intervals around principal components of the empirical posterior distribution, forming an ambiguity region that guarantees the inclusion of true unseen values with a user-defined confidence probability. To improve computational efficiency and interpretability, we also guarantee the recovery of true unseen values using only a few principal directions, resulting in more informative uncertainty regions. Our approach is verified through experiments on image colorization, super-resolution, and inpainting; its effectiveness is shown through comparison to baseline methods, demonstrating significantly tighter uncertainty regions.

Oral pathology following treatment of brain tumors in childhood

Relevance. The limited selectivity of antitumor therapy for central nervous system neoplasms entails the risk of dental morbidity in patients.The aim of the study is to assess the severity of damage to the dental hard tissues in patients with tumors of the posterior cranial fossa (medulloblastoma, astrocytoma) and other tumors of the central nervous system.Materials and methods. Three comparison groups were established based on the toxicity profiles of antitumor therapy among 251 patients at the "Russian Field" treatment and rehabilitation scientific center, to assess the characteristics of oral health status. The assessment of oral health status included diagnosing the carious process and calculating the DMFT index. The degree of damage to the dental hard tissues was evaluated on a four-point scale. The clinical manifestations of oral cavity pathology were compared using the Z-criterion for multiple pairwise comparisons of data, with adjustments for multiple testing (Multiple pairwise comparison Z-test for proportions with multiple testing correction) using the free R software version 3.5.2. The null hypothesis of equal indicators in the compared groups was rejected with a 95% confidence probability when comparing groups of patients.Results. Dental hard tissue pathology was significantly more prevalent in patients with medulloblastoma compared to those diagnosed with astrocytomas and other central nervous system tumors. Additionally, the prevalence of dental hard tissue pathology progressively decreased as the observation period following the completion of antitumor remission was extended.Conclusion. The most severe damage to dental hard tissues is observed in patients with medulloblastoma, who undergo more intensive antitumor therapy compared to those with CNS tumors of other histological types.

ВПЛИВ УМОВ РОБОТИ НА ТЕМПЕРАТУРНИЙ РЕЖИМ ПІДШИПНИКІВ З МЕТАЛЕВИМИ ТА КЕРАМІЧНИМИ КУЛЬКАМИ

The results of an experimental study of the temperature mode of operation of rolling bearings under different lubrication conditions are presented. Comparative tests of all-metal bearings and similar bearings with rolling elements made of silicon nitride (hybrid bearings) when lubricated with an oil jet and an oil-air mixture in the range of rotor rotation frequencies from 0 to 35000 rpm were carried out. For the experiments, a specially created set was used, which allows you to study the temperature of the bearings and the temperature of the lubricant at the entrance and exit from the bearings. The axial load on the bearings was created with the help of a compression spring, which rested on the outer rings of the bearings. This made it possible to close the load inside the system. During the tests, the load was 0 N, 1000 N, and 2000 N. Tests were carried out for two modes of bearing lubrication: oil jet and oil-air mixture. The method of measuring the temperature of the outer ring of bearings is described. The temperature measurement error was considered as a complex of instrumental error and method error. It was assumed that the temperature error is subject to a normal distribution. The conducted analysis showed that the error of measuring the temperature of the bearing ring was ± 2.8 °C with a confidence probability of 95 %. It has been established that bearings with ceramic balls in the entire studied range behave qualitatively and quantitatively in approximately the same way as with steel ones. The temperature level of hybrid bearings is slightly higher when lubricated with both an oil jet and an oil-air mixture. There is a clear connection between the values of the bearing temperature and the moment of resistance in the bearing: the increase in temperature is observed under the same conditions under which the moment of resistance increases.

Hardware method for zero optical path difference position detection of FTIR spectrometer

This paper presents a method for accurately controlling the position of the zero optical path difference in the transformation process from interferogram of Fourier transform infrared (FTIR) spectrometer to spectrogram. This position has a significant impact on the position, consistency, and repeatability of spectral lines in restored spectrograms. The proposed method, known as the double-slit photoelectric aiming and triggering method (DS-PATM), can be used to precisely control the zero optical path difference position, as well as the starting and ending positions of sampling, thereby ensuring identical optical path difference in the spectrometer system. The working principle of DS-PATM is elaborated in detail, followed by a description of the optical structure of the photoelectric aiming detector and the photoelectric aiming trigger circuit. Subsequently, a displacement detection system of the spectrometer moving mirror is developed. The system is experimentally validated through displacement aiming and triggering experiments of the moving mirror of the FTIR spectrometer, conducted under standard measurement environment. The results demonstrate that using the proposed method, the standard deviation of zero optical path difference position can be measured with a confidence probability (math.) of 99.7% and a resolution of 0.1 cm−1 for Fourier transform spectrometer, yielding a value of 0.08 μm. This confirms the effectiveness of the detection system and its ability to meet the requirements of sampling interferogram with high resolution.

Оценка параметров по засоренным выборкам

Parametric estimates based on contaminated samples are considered in the paper. The paper provides an over-view of algorithms for estimating the mean and the variance for a one-dimensional sample, as well as estimating the mean vector and the covariance matrix for a multidimensional sample. The paper uses the Minimal Covariance Determinant (MCD) algorithm adapted for one-dimensional sample and the MCD algorithm for multidimensional sample. The parameters are estimated on a subsample, the size of which is determined by a given confidence probability. Examples for samples with different levels of contamination are considered. In both examples, the sample was a union of two subsamples. The first subsample, the main one, was generated by normal distribution laws. The second subsample, auxiliary, was generated by different distribution laws. The examples demonstrate the dependence of the estimation accuracy on the confidence level and contamination. The figures illustrate the operation of the MCD algorithm. The main idea of the paper is to show the robustness of the MCD algorithm.

Studying the estimates of gamma distribution parameters

The main goal of the work is to obtain additional information about the experimentally obtained sample with a previously known theoretical distribution, the point estimates of the parameters of which are considered known. At the same time, the laws of distribution of these parameters remain unknown, whereas they could provide a researcher with additional information about both the material and technological processes. Hence, it is necessary to obtain an additional number of samples, which is not always possible experimentally. Here we used data on the service life of cutters (GOST 11.011–83 «Rules for determining estimates and confidence limits for gamma distribution parameters») as an experimental sample. The experimental sample contains the results of 50 measurements. The mean was 57.88 hours CI [50.74:65.01]. The confidence probability is taken to be 0.95. Bootstrap was used as a way to obtain additional samples. The universal mathematical package MATLAB is used in the study. Bootstrap allows generation of a large number of samples that require certain selection rules to be applied to them. The first obvious requirement is the significance of the correlation coefficient of the generated sample with the original one. Even at this stage, the bootstrap showed certain limitations in performing the task set in the study. For 1000 samples generated by the standard bootstrap routine, the mean for the population of all mean bootstrap samples was 57.80 hours, and the confidence interval was [50.59:58.08]. The result is good. Though the nonparametric hypothesis regarding an agreement between the bootstrap samples for the gamma distribution and the parameters characteristic of the original experimentally obtained sample was not rejected, the statistically significant correlation coefficient was observed only for 29 bootstrap samples. As a result of meeting these obvious requirements, less than 3% of the generated bootstrap samples remained for further consideration. This fact requires the introduction of additional conditions when using the bootstrap to obtain samples that are close to the original experimental sample, which can be rather specific. To determine the parameters of the gamma distribution for bootstrap samples, the method of moments and the one-step method were used.