Effective Model Research Articles

Machine learning tools in the tasks of selecting the determinants of socio-economic situation and development potential of Russian regions

Subject. The article addresses the selection of determinants that are significant for assessing the level of socio-economic situation and development potential of Russian regions. Objectives. The aim is to study machine learning algorithms for the selection of determinants – predictors of the level of socio-economic situation and development potential of Russian regions, to build models of classification of regions, according to the level of socio-economic situation, using various machine learning algorithms. Methods. To build classification models, I used data from the Federal State Statistics Service, the Institute of Scientific Communications, the RIA Novosti news agency, and the TAdviser Internet portal. Procedures for data classification, model parameter estimation, selection of significant determinants and visualization of results are performed, using the basic functions of the PyCaret library. Cohen's Kappa statistics and Matthews correlation coefficient were employed as priority metrics for evaluating the model productivity. The algorithms for selecting determinants are implemented in the Google Colab analytical environment. Results. I constructed multiclass classification models based on simple and ensemble machine learning algorithms. Simple classification algorithms, including logistic and ridge regression models, naive Bayesian algorithm, decision tree, support vector machine, and k-nearest neighbor methods are characterized by accuracy values at 77%, however, Cohen's Kappa statistics and the Matthews correlation coefficient only show a satisfactory relationship between the actual and predicted value of the region class. Ensemble algorithms, including random forest, gradient boosting and extreme gradient boosting, are characterized by a close relationship between the actual and forecast estimates of the classifier at a level of more than 70%. Conclusions. The random forest algorithm is recognized as the most effective classification model. The gross regional product and investments in fixed assets are informative determinants for measuring the socio-economic status.

Development of a psychological health promotion intervention for ultra-orthodox Jewish mothers of children with ADHD using the intervention mapping protocol

BackgroundAttention Deficit Hyperactivity Disorder (ADHD) is a common neuro-developmental health condition in children and adolescents, in which its associated behavior manifestations are known to negatively affect members of the family unit, especially mothers. Ultra-orthodox Jewish (UOJ) community is growing globally and mothers of children with ADHD in this community are potentially at risk for negative health outcomes. As the UOJ community is culturally conservative, maintaining a distinct separation from outside influences, they often avoid utilizing public mental health services due to stigma and a lack of culturally sensitive treatments. Thus, this study aimed to develop a theory-driven and culturally appropriate psychological health promotion intervention for these mothers using the Intervention Mapping protocol.MethodsA mixed-method design was used. Qualitative content analysis was performed on four focus groups (n=25). Additionally, descriptive statistics including the content validity index was used to measure feedback regarding the developed intervention protocol’s relevance, effectiveness, and appropriateness Theoretical models for behavior change, including the Behavior Change Wheel’s COM-B system and the Theoretical Domains Framework, and literature on ADHD in the general population and the ultra-orthodox community were integrated in the process. Intervention components were systematically derived from findings.ResultsKey determinants of health behavior change were identified, resulting in formulating intervention objectives addressing stigma reduction surrounding ADHD, increasing knowledge about the ADHD condition and treatment, awareness of the school systems’ capabilities in meeting the ADHD child’s needs, enhancing mothers’ advocacy skills, and maternal self-care. Intervention? strategies included a group setting, providing information on health consequences, social support, re-attribution, active learning, goal setting, and promoting an identity associated with the desired behavior change. Mothers’ quantitative feedback confirmed the overall relevance, effectiveness, and appropriateness of the interventions’ content (CVIavg= .86, .85, .87).ConclusionsIntervention Mapping facilitated the development of a culturally sensitive psychological health promotion intervention for ultra-orthodox Jewish mothers of children with ADHD. Further research is warranted to assess intervention feasibility and effectiveness.

Moduli spaces of instantons in flag manifold sigma models. Vortices in quiver gauge theories

In this paper, we discuss lumps (sigma model instantons) in flag manifold sigma models. In particular, we focus on the moduli space of BPS lumps in general Kähler flag manifold sigma models. Such a Kähler flag manifold, which takes the form Un1+⋯+nL+1Un1×⋯×UnL+1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\frac{\ extrm{U}\\left({n}_1+\\cdots +{n}_{L+1}\\right)}{\ extrm{U}\\left({n}_1\\right)\ imes \\cdots \ imes \ extrm{U}\\left({n}_{L+1}\\right)} $$\\end{document}, can be realized as a vacuum moduli space of a U(N1) × ··· × U(NL) quiver gauged linear sigma model. When the gauge coupling constants are finite, the gauged linear sigma model admits BPS vortex configurations, which reduce to BPS lumps in the low energy effective sigma model in the large gauge coupling limit. We derive an ADHM-like quotient construction of the moduli space of BPS vortices and lumps by generalizing the quotient construction in U(N) gauge theories by Hanany and Tong. As an application, we check the dualities of the 2d models by computing the vortex partition functions using the quotient construction.

Dynamic selection of machine learning models for time-series data

Adapting to changes in time-series data is a major challenge in machine learning. This problem is particularly acute in the case of limited computing power (e.g., edge devices) that does not enable the independent training of new models. While multiple studies attempt to adequately train a machine learning model on rapidly shifting data (i.e., concept drift), the challenge of dynamically selecting the most effective machine learning model for future time steps has been largely overlooked. In this study, we propose Adaptive machine learning for Dynamic ENvironments (ADEN ), a method for analyzing future trends in time-series data and selecting the most suitable ML model. Our approach models multiple aspects of the analyzed data and analyzes the behavior of multiple machine learning models on earlier time steps. By training ADEN on multiple datasets, we can deploy a zero-shot model that does not require additional training when applied to new datasets. Our evaluation, conducted on 46 time-series classification datasets, shows that ADEN not only outperforms all baselines in terms of average performance but is also capable of avoiding sudden drops in performance that characterize all other evaluated algorithms.

Ergodicity Breaking Under Confinement in Cold-Atom Quantum Simulators

The quantum simulation of gauge theories on synthetic quantum matter devices has gained a lot of traction in the last decade, making possible the observation of a range of exotic quantum many-body phenomena. In this work, we consider the spin-1/2 quantum link formulation of 1+1D quantum electrodynamics with a topological θ-angle, which can be used to tune a confinement-deconfinement transition. Exactly mapping this system onto a PXP model with mass and staggered magnetization terms, we show an intriguing interplay between confinement and the ergodicity-breaking paradigms of quantum many-body scarring and Hilbert-space fragmentation. We map out the rich dynamical phase diagram of this model, finding an ergodic phase at small values of the mass μ and confining potential χ, an emergent integrable phase for large μ, and a fragmented phase for large values of both parameters. We also show that the latter hosts resonances that lead to a vast array of effective models. We propose experimental probes of our findings, which can be directly accessed in current cold-atom setups.

East Asia Atmospheric River Forecast With a Deep Learning Method: GAN‐UNet

AbstractAccurate forecasting of atmospheric rivers (ARs) holds significance in preventing losses from extreme precipitation. However, traditional numerical weather prediction (NWP) models are computationally expensive and can be limited in accuracy due to inaccurate physical parameter settings. To overcome these limitations, we propose a deep learning (DL) model, called GAN‐UNet, to forecast the AR occurrence, position, and intensity in East Asia. GAN‐UNet can capture the complex nonlinear relationship between the inputs at the past moment, including the vertically integrated water vapor transport (IVT), zonal wind at 850 hPa (U850), and meridional wind at 850 hPa (V850), and the forecast output (IVT, U850, or V850), whose results are comparable to NWP models. In addition, the average model (AM) by integrating the results generated by GAN‐UNet and European Centre for Medium‐Range Weather Forecasts (ECMWF) outperforms all the NWP models selected in this study, demonstrating its potential to improve the performance of NWP through the DL method. Specifically, the 5‐day average F1 scores of the AM are 0.777 and 0.845, whose values are significantly better than those obtained by ECMWF (0.712 and 0.794) in the two key regions of East Asia; The AM 5‐day average intersection over unions are 0.706 and 0.688 while the values of ECMWF are 0.675 and 0.64; in terms of intensity forecast, GAN‐UNet and AM exhibited lower differences in most of the intensity bins, except for the final bin with IVT more than 825 kg m−1 s−1. With this thorough analysis, GAN‐UNet is shown as an effective model to forecast ARs.

Systematic study of two proton radioactivity within the effective liquid drop model

In this study, we investigated two-proton radioactivity using the effective liquid drop model with varying mass asymmetry shapes and effective inertia coefficients (VMAS-EFF). Gaudin’s expression term for Coulomb potential energy is replaced in the current study’s calculation of potential energy. The VMAS-EFF combination predicts the half-life with the lowest standard deviation ( σ =1.03) compared to other models and previous constant mass asymmetry with Werner-inertial Wheeler’s coefficient combinations. The lowest standard deviation value indicates that the VMAS-EFF combination is suitable for reproducing the experimental data. We extended the half-life predictions to another 54 nuclei and compared them with six theoretical model predictions and two empirical formula predictions. The present work also investigated the decay possibilities using quantum tunnelling by computing the driving potential (V-Q) for various states of angular momentum l = 0, 2, 3, and 4. Found that 6Be, 6B, 8C, 12O,15,16Ne, 19Mg, 22Si, 26S, 30Ar 38,39Ti, 40V, 42Cr, 45Fe, 48,49Ni, 54Zn, 58,59,60Ge and 64Se nuclei have a high chance of emission for two protons, while 7B, 10N, 14F, 17Na, 24P, 28Cl,32K, 34Ca, 36,37Sc, 44Mn, 47Co, 52Cu, 56,57,58Ga, 61,62As nuclei have a lower chance for two proton than one proton decay. The computation of separation energy findings agree well with the driving potential findings.

Transfer Learning with EfficientNetV2S for Automatic Face Shape Classification

The classification of human face shapes, a pivotal aspect of one’s appearance, plays a crucial role in diverse fields like beauty, cosmetics, healthcare, and security. In this paper, we present a multi-step methodology for face shape classification, harnessing the potential of transfer learning and a pretrained EfficientNetV2S neural network. Our approach comprises key phases, including preprocessing, augmentation, training, and testing, ensuring a comprehensive and reliable solution. The preprocessing step involves precise face detection, cropping, and image scaling, laying a solid foundation for accurate feature extraction. Our methodology utilizes a publicly available dataset of female celebrities, comprising five face shape classes: heart, oblong, oval, round, and square. By augmenting this dataset during training, we magnify its diversity, enabling better generalization and enhancing the model’s robustness. With the EfficientNetV2S neural network, we employ transfer learning, leveraging pretrained weights to optimize accuracy, training speed, and parameter size. The result is a highly efficient and effective model, which outperforms state-of-the-art approaches on the same dataset, boasting an outstanding overall accuracy of 96.32%. Our findings demonstrate the efficiency of our approach, proving its potential in the field of face shape classification. The success of our methodology holds promise for various applications, offering valuable insights into beauty analysis, cosmetic recommendations, and personalized healthcare.

A Fault-Tolerant Soft Sensor Algorithm Based on Long Short-Term Memory Network for Uneven Batch Process

Batch processing is a widely utilized technique in the manufacturing of high-value products. Traditional methods for quality assessment in batch processes often lead to productivity and yield losses because of offline measurement of quality variables. The use of soft sensors enhances product quality and increases production efficiency. However, due to the uneven batch data, the variation in processing times presents a significant challenge for building effective soft sensor models. Moreover, sensor failures, exacerbated by the manufacturing environment, complicate the accurate modeling of process variables. Existing soft sensor approaches inadequately address sensor malfunctions, resulting in significant prediction inaccuracies. This study proposes a fault-tolerant soft sensor algorithm that integrates two Long Short-Term Memory (LSTM) networks. The algorithm focuses on modeling process variables and compensating for sensor failures using historical batch quality data. It introduces a novel method for converting quality variables into process rates to align uneven batch data. A case study on simulated penicillin production validates the superiority of the proposed algorithm over conventional methods, showing its capacity for precise endpoint detection and effectiveness in addressing the challenges of batch process quality assurance. This study offers a robust solution to the issues of soft sensor reliability and data variability in industrial manufacturing.

Software Implementation of Methods for Modeling the Interaction of Electromagnetic Radiation with Carbon Nanocomposites

Methods for modeling the interaction of electromagnetic radiation with a carbon nanostructured composite containing various structural elements, such as carbon fibers, nanoparticles, air gaps, and transition shells are considered. It is shown that for effective and adequate modeling of the interaction of electromagnetic radiation with a carbon nanostructured composite, it is necessary to create software due to the presence of a large set of input parameters, as well as the complexity of calculations. Functional requirements for the software are formulated, reflecting the choice of models and the presence of a convenient interactive interface. The architecture of the software that implements models of interaction of electromagnetic radiation with a carbon nanostructured composite is described, and the software implementation (class structure, methods, user interface) is considered. The developed code is based on proven physical and mathematical models, which make it possible to obtain results close to experimental data. The software architecture allows to effectively manage the modeling process, and also includes the ability to modify models and take into account new structural elements. An interactive interface for users has been developed that allows to easily manage the model parameters and analyze the results of numerical experiments.

Insights on the Use of Transgenic Mice Models in Alzheimer's Disease Research.

Alzheimer's disease (AD), the leading cause of dementia, presents a significant global health challenge with no known cure to date. Central to our understanding of AD pathogenesis is the β-amyloid cascade hypothesis, which underlies drug research and discovery efforts. Despite extensive studies, no animal models of AD have completely validated this hypothesis. Effective AD models are essential for accurately replicating key pathological features of the disease, notably the formation of β-amyloid plaques and neurofibrillary tangles. These pathological markers are primarily driven by mutations in the amyloid precursor protein (APP) and presenilin 1 (PS1) genes in familial AD (FAD) and by tau protein mutations for the tangle pathology. Transgenic mice models have been instrumental in AD research, heavily relying on the overexpression of mutated APP genes to simulate disease conditions. However, these models do not entirely replicate the human condition of AD. This review aims to provide a comprehensive evaluation of the historical and ongoing research efforts in AD, particularly through the use of transgenic mice models. It is focused on the benefits gathered from these transgenic mice models in understanding β-amyloid toxicity and the broader biological underpinnings of AD. Additionally, the review critically assesses the application of these models in the preclinical testing of new therapeutic interventions, highlighting the gap between animal models and human clinical realities. This analysis underscores the need for refinement in AD research methodologies to bridge this gap and enhance the translational value of preclinical studies.

Automatic quantitative stroke severity assessment based on Chinese clinical named entity recognition with domain-adaptive pre-trained large language model

Background:Stroke is a prevalent disease with a significant global impact. Effective assessment of stroke severity is vital for an accurate diagnosis, appropriate treatment, and optimal clinical outcomes. The National Institutes of Health Stroke Scale (NIHSS) is a widely used scale for quantitatively assessing stroke severity. However, the current manual scoring of NIHSS is labor-intensive, time-consuming, and sometimes unreliable. Applying artificial intelligence (AI) techniques to automate the quantitative assessment of stroke on vast amounts of electronic health records (EHRs) has attracted much interest. Objective:This study aims to develop an automatic, quantitative stroke severity assessment framework through automating the entire NIHSS scoring process on Chinese clinical EHRs. Methods:Our approach consists of two major parts: Chinese clinical named entity recognition (CNER) with a domain-adaptive pre-trained large language model (LLM) and automated NIHSS scoring. To build a high-performing CNER model, we first construct a stroke-specific, densely annotated dataset “Chinese Stroke Clinical Records” (CSCR) from EHRs provided by our partner hospital, based on a stroke ontology that defines semantically related entities for stroke assessment. We then pre-train a Chinese clinical LLM coined “CliRoberta” through domain-adaptive transfer learning and construct a deep learning-based CNER model that can accurately extract entities directly from Chinese EHRs. Finally, an automated, end-to-end NIHSS scoring pipeline is proposed by mapping the extracted entities to relevant NIHSS items and values, to quantitatively assess the stroke severity. Results:Results obtained on a benchmark dataset CCKS2019 and our newly created CSCR dataset demonstrate the superior performance of our domain-adaptive pre-trained LLM and the CNER model, compared with the existing benchmark LLMs and CNER models. The high F1 score of 0.990 ensures the reliability of our model in accurately extracting the entities for the subsequent automatic NIHSS scoring. Subsequently, our automated, end-to-end NIHSS scoring approach achieved excellent inter-rater agreement (0.823) and intraclass consistency (0.986) with the ground truth and significantly reduced the processing time from minutes to a few seconds. Conclusion:Our proposed automatic and quantitative framework for assessing stroke severity demonstrates exceptional performance and reliability through directly scoring the NIHSS from diagnostic notes in Chinese clinical EHRs. Moreover, this study also contributes a new clinical dataset, a pre-trained clinical LLM, and an effective deep learning-based CNER model. The deployment of these advanced algorithms can improve the accuracy and efficiency of clinical assessment, and help improve the quality, affordability and productivity of healthcare services.

Temporal cluster-based local deep learning or signal processing-temporal convolutional transformer for daily runoff prediction?

Water scarcity poses a major obstacle to sustainable development, and precise discharge prediction plays a vital role in enabling effective water resource management. This study investigated improved prediction techniques for nonstationary time series. The study evaluated the effect of signal processing techniques and blending approaches on the performance of deep learning models for daily discharge prediction. It also compared the performance of cluster-based local modeling with hybrid signal processing-deep learning approaches. Two robust deep learning methods, Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), along with a powerful signal processing approach called discrete wavelet transform, were utilized for prediction of daily discharge. Three blending approaches were assessed: 1) decomposing both inputs and target, 2) decomposing only the target, and 3) decomposing only the inputs and then blending them with deep learning models. Also, a new hybrid deep learning based model namely discrete wavelet transform-Temporal Convolutional Transformer (DWT-TCT) was developed. The results showed that a single-output wavelet transform-deep learning model (3rd blending approach) outperformed multi-output models, demonstrating a relative enhancement of up to 56% for the LSTM model and 51% for the CNN model. Furthermore, temporal cluster-based local modeling displayed promising performance, resulting in an improvement of up to 18% in NRMSE compared to the wavelet transform-deep learning model, while also requiring less computational cost. The successful results of the temporal cluster-based local modeling approach provide a beneficial alternative to hybrid signal processing-deep learning models. In addition the results showed that the proposed DWT-TCT model outperformed all other models with NRMSE ranges from 6.8% to 16.2% in the study areas. The results have implications for hydrology and water resources management, as they can be used to develop more precise and effective models for predicting discharge in view of nonstationarity.

Atomic soliton transmission and induced collapse in scattering from a narrow barrier

We report systematic numerical simulations of the collision of a bright matter-wave soliton made of Bose-condensed alkali-metal atoms through a narrow potential barrier by using the three-dimensional Gross–Pitaevskii equation. In this way, we determine how the transmission coefficient depends on the soliton impact velocity and the barrier height. Quite remarkably, we also obtain the regions of parameters where there is the collapse of the bright soliton induced by the collision. We compare these three-dimensional results with the ones obtained by three different one-dimensional nonlinear Schrödinger equations. We find that a specifically modified nonpolynomial Schrödinger equation is able to accurately assess the transmission coefficient even in a region in which the usual nonpolynomial Schrödinger equation collapses. In particular, this simplified but very effective one-dimensional model takes into account the transverse width dynamics of the soliton with an ordinary differential equation coupled to the partial differential equation of the axial wave function of the Bose–Einstein condensate.

PCTDepth: Exploiting Parallel CNNs and Transformer via Dual Attention for Monocular Depth Estimation

Monocular depth estimation (MDE) has made great progress with the development of convolutional neural networks (CNNs). However, these approaches suffer from essential shortsightedness due to the utilization of insufficient feature-based reasoning. To this end, we propose an effective parallel CNNs and Transformer model for MDE via dual attention (PCTDepth). Specifically, we use two stream backbones to extract features, where ResNet and Swin Transformer are utilized to obtain local detail features and global long-range dependencies, respectively. Furthermore, a hierarchical fusion module (HFM) is designed to actively exchange beneficial information for the complementation of each representation during the intermediate fusion. Finally, a dual attention module is incorporated for each fused feature in the decoder stage to improve the accuracy of the model by enhancing inter-channel correlations and focusing on relevant spatial locations. Comprehensive experiments on the KITTI dataset demonstrate that the proposed model consistently outperforms the other state-of-the-art methods.

Residual U-Net approach for thyroid nodule detection and classification from thyroid ultrasound images

With so many thyroid knobs (nodules) discovered by accident, it is critical to recognize as many aberrant knobs (nodules) as possible from fine-needle aspiration (FNA) biopsies or other medical procedures while excluding those that are virtually certainly benign. Thyroid ultrasonography, on the other hand, is prone to interobserver variability and subjective translations. An effective deep learning model for segmenting and categorizing thyroid nodules in this study follows the stages below: data collection from a well-known archive, The Thyroid Digital Image Database (TDID), which comprises ultrasound pictures from 298 patients, preprocessing using anisotropic diffusion filter (ADF) for removing noise and enhancing the images, segmentation using a bilateral filter for segmenting images, feature extraction using grey level occurrence matrix (GLCM), feature selection using Multi-objective Particle Swarm with Random Forest Optimization (MbPSRA) and finally classification happens were Residual U-Net will be used. Experiment evaluation states the proposed model outperforms well than other state-of-art models.

Machine learning aided uncertainty quantification for engineering structures involving material-geometric randomness and data imperfection

In real-world engineering, uncertainty is ubiquitous within material properties, structural geometry, load conditions, and the like. These uncertainties have substantial impacts on the estimation of structural performance. Furthermore, information or datasets in real life commonly contain imperfections, e.g., noise, outliers, or missing data. To quantify these impacts induced by uncertainties on structural behaviours and reduce the effects of data imperfections simultaneously, a machine learning-aided stochastic analysis framework is proposed. A novel supervised machine learning technique, namely the Capped Extended Support Vector Regression (CX-SVR) technique, is developed to effectively suppress the effects of outliers and noise in datasets. Its inherent convexity in optimization and capped strategy theoretically supports the accuracy of CX-SVR, especially in handling datasets with imperfections. Once the effective surrogate model is established, subsequent analyses, like sampling-based methods, can circumvent the cumbersome physical model, which is potentially the nest of computational burden and errors in engineering applications. The high robustness of the proposed approach can be summarized in four main aspects: unrestrictive selection of the system inputs and their statistical information, ‘perfect’ or ‘imperfect’ data, enough statistical information (including statistical moments, probability density functions, and cumulative distribution functions) of the system outputs, and physical problems from various engineering fields.

Dosage-dependent effects of FGFR2W290R mutation on craniofacial shape and cellular dynamics of the basicranial synchondroses.

Craniosynostosis is a common yet complex birth defect, characterized by premature fusion of the cranial sutures that can be syndromic or nonsyndromic. With over 180 syndromic associations, reaching genetic diagnoses and understanding variations in underlying cellular mechanisms remains a challenge. Variants of FGFR2 are highly associated with craniosynostosis and warrant further investigation. Using the missense mutation FGFR2W290R , an effective mouse model of Crouzon syndrome, craniofacial features were analyzed using geometric morphometrics across developmental time (E10.5-adulthood, n = 665 total). Given the interrelationship between the cranial vault and basicranium in craniosynostosis patients, the basicranium and synchondroses were analyzed in perinates. Embryonic time points showed minimal significant shape differences. However, hetero- and homozygous mutant perinates and adults showed significant differences in shape and size of the cranial vault, face, and basicranium, which were associated with cranial doming and shortening of the basicranium and skull. Although there were also significant shape and size differences associated with the basicranial bones and clear reductions in basicranial ossification in cleared whole-mount samples, there were no significant alterations in chondrocyte cell shape, size, or orientation along the spheno-occipital synchondrosis. Finally, shape differences in the cranial vault and basicranium were interrelated at perinatal stages. These results point toward the possibility that facial shape phenotypes in craniosynostosis may result in part from pleiotropic effects of the causative mutations rather than only from the secondary consequences of the sutural defects, indicating a novel direction of research that may shed light on the etiology of the broad changes in craniofacial morphology observed in craniosynostosis syndromes.

Features of Pedagogical Modeling of the Educational Process in the System of Additional Professional Education of Medical Workers

The modern system of additional professional education offers new principles and approaches to organising the pedagogical process. In this regard, the issue of choosing the most appropriate technologies and tools for organising training and building an effective model of the educational process in the system of additional professional education of medical workers is relevant. The purpose of the study is to identify the features of pedagogical modeling of the educational process in the system of additional professional education of medical workers. Research methodology and methods. The article discusses theoretical and conceptual modeling ideas presented in domestic and foreign literature. The study was conducted on the basis of the Federal State Budgetary Educational Institution of Higher Education “Novosibirsk State Medical University” of the Ministry of Health of Russia in the Centre for Distance Educational Technologies of the Department of Postgraduate Education. Research Results. During the study, it was revealed that pedagogical modeling of the educational process in the system of additional professional education of medical specialists has its own characteristics. Conclusion. When modeling the educational process in the system of additional professional education, the current requirements and needs of medical specialists must be taken into account, which makes it possible to adapt the content and methodology of training to specific professional tasks. Besides, it is important to consider the practice-focused nature of medical education: internships, training practical skills on simulators, active use of digital educational technologies, the possibility of individualising training so that all students could develop their professional skills according to their needs. The system is for assessing academic performance and monitoring student satisfaction with training, which allows to adjust the educational process and ensure high quality education is an important part of pedagogical modeling.

High harmonic generation in graphene quantum dots

We study theoretically the generation of high harmonics in disk graphene quantum dots placed in linearly polarized short pulse. The quantum dots (QD) are described within an effective model of the Dirac type and the length gauge was used to describe the interaction of quantum dots with an optical pulse. The generated radiation spectra of graphene quantum dots can be controlled by varying the quantum dot size, i.e. its radius. With increasing the quantum dot radius, the intensities of low harmonics mainly decrease, while the cutoff frequency increases. The sensitivity of the cutoff frequency to the QD size increases with the intensity of the pulse.