Discrete Nature Research Articles

Extending the QMM Framework to the Strong and Weak Interactions

We extend the Quantum Memory Matrix (QMM) framework, originally developed to reconcile quantum mechanics and general relativity by treating space–time as a dynamic information reservoir, to incorporate the full suite of Standard Model gauge interactions. In this discretized, Planck-scale formulation, each space–time cell possesses a finite-dimensional Hilbert space that acts as a local memory, or quantum imprint, for matter and gauge field configurations. We focus on embedding non-Abelian SU(3)c (quantum chromodynamics) and SU(2)L× U(1)Y (electroweak interactions) into QMM by constructing gauge-invariant imprint operators for quarks, gluons, electroweak bosons, and the Higgs mechanism. This unified approach naturally enforces unitarity by allowing black hole horizons, or any high-curvature region, to store and later retrieve quantum information about color and electroweak charges, thereby preserving subtle non-thermal correlations in evaporation processes. Moreover, the discretized nature of QMM imposes a Planck-scale cutoff, potentially taming UV divergences and modifying running couplings at trans-Planckian energies. We outline major challenges, such as the precise formulation of non-Abelian imprint operators and the integration of QMM with loop quantum gravity, as well as possible observational strategies—ranging from rare decay channels to primordial black hole evaporation spectra—that could provide indirect probes of this discrete, memory-based view of quantum gravity and the Standard Model.

Daylight Saving Time and Automobile Accidents: Evidence From Chile.

Under the evidence that the Daylight Saving Time (DST) regime does not accomplish its primary goal of saving energy, I analyze one of the main side effects, automobile accidents in Chile between 2002 and 2018. I use a Regression Discontinuity Design (RDD) exploiting the discrete nature of the transition into DST and a Difference-in-Difference (DID) approach, taking advantage of the changes in dates that the policy starts and ends over the years. I find a 2.7% reduction in automobile accidents under the DST regime. I isolate the two main mechanisms: sleep disruption and the reallocation of light. I find suggestive evidence that the sleep disruption effect plays a relevant role at both transitions: it increases automobile accidents by 6% the first week following the transition into DST and decreases them by 3.9% the first week following the transition into Standard Time (ST). Ialso find evidence that ambient light reduces serious and fatal accident risk.

Fractional tackles: leveraging player tracking data for within-play tackling evaluation in American football

Tackling is a fundamental defensive move in American football, with the main purpose of stopping the forward motion of the ball-carrier. However, current tackling metrics are manually recorded outcomes that are inherently flawed due to their discrete and subjective nature. Using player tracking data, we present a novel framework for assessing tackling contribution in a continuous and objective manner. Our approach first identifies when a defender is in a “contact window” of the ball-carrier during a play, before assigning value to each window and the players involved. This enables us to devise a new metric called fractional tackles, which credits defenders for halting the ball-carrier’s forward motion toward the end zone. We demonstrate that fractional tackles overcome the shortcomings of traditional metrics such as tackles and assists, by providing greater variation and measurable information for players lacking recorded statistics like defensive linemen. We view our contribution as a significant step forward in measuring defensive performance in American football and a clear demonstration of the capabilities of player tracking data.

A State-of-the-Art Fractional Order-Driven Differential Evolution for Wind Farm Layout Optimization

The wind farm layout optimization problem (WFLOP) aims to maximize wind energy utilization efficiency and mitigate energy losses caused by wake effects by optimizing the spatial layout of wind turbines. Although Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) have been widely used in WFLOP due to their discrete optimization characteristics, they still have limitations in global exploration capability and optimization depth. Meanwhile, the Differential Evolution algorithm (DE), known for its strong global optimization ability and excellent performance in handling complex nonlinear problems, is well recognized in continuous optimization issues. However, since DE was originally designed for continuous optimization scenarios, it shows insufficient adaptability under the discrete nature of WFLOP, limiting its potential advantages. In this paper, we propose a Fractional-Order Difference-driven DE Optimization Algorithm called FODE. By introducing the memory and non-local properties of fractional-order differences, FODE effectively overcomes the adaptability issues of advanced DE variants in WFLOP’s discreteness while organically applying their global optimization capabilities for complex nonlinear problems to WFLOP to achieve more efficient overall optimization performance. Experimental results show that under 10 complex wind farm conditions, FODE significantly outperforms various current state-of-the-art WFLOP algorithms including GA, PSO, and DE variants in terms of optimization performance, robustness, and applicability. Incorporating more realistic wind speed distribution and wind condition data into modeling and experiments, further enhancing the realism of WFLOP studies presented here, provides a new technical pathway for optimizing wind farm layouts.

Robust assessment of the cortical encoding of word-level expectations using the temporal response function

Objective. Speech comprehension involves detecting words and interpreting their meaning according to the preceding semantic context. This process is thought to be underpinned by a predictive neural system that uses that context to anticipate upcoming words. However, previous studies relied on evaluation metrics designed for continuous univariate sound features, overlooking the discrete and sparse nature of word-level features. This mismatch has limited effect sizes and hampered progress in understanding lexical prediction mechanisms in ecologically-valid experiments.Approach. We investigate these limitations by analyzing both simulated and actual electroencephalography (EEG) signals recorded during a speech comprehension task. We then introduce two novel assessment metrics tailored to capture the neural encoding of lexical surprise, improving upon traditional evaluation approaches.Main results. The proposed metrics demonstrated effect-sizes over 140% larger than those achieved with the conventional temporal response function (TRF) evaluation. These improvements were consistent across both simulated and real EEG datasets.Significance. Our findings substantially advance methods for evaluating lexical prediction in neural data, enabling more precise measurements and deeper insights into how the brain builds predictive representations during speech comprehension. These contributions open new avenues for research into predictive coding mechanisms in naturalistic language processing.

Women's experiences and acceptability of self-administered, home delivered, intravaginal 5-Fluorouracil cream for cervical precancer treatment in Kenya.

Innovative strategies are essential to meet the World Health Organization's 90/70/90 cervical cancer elimination targets, aiming for 90% access to precancer treatment globally by 2030. In low-and middle-income countries (LMICs) where most cervical cancer cases occur, access to precancer treatment is severely limited. Scalable solutions like self-administered topical therapies can help close this gap. In a recent Phase I trial (ClinicalTrials.gov NCT05362955), we demonstrated safety and adherence to self-administered intravaginal 5% 5-Fluorouracil (5FU) cream as an adjuvant therapy for cervical precancer among women living with HIV (WLWH) in rural Kenya. To understand women's experiences with self-administered 5FU, we evaluated the acceptability of this intervention among trial participants. All 12 participants from the Phase I trial completed a structured questionnaire and in-depth semi-structured interviews in their preferred language, focusing on their experiences with 5FU self-administration, challenges faced, and overall acceptability of the intervention, including whether they would use it again or recommend it to someone who needed it. Quantitative data were analyzed using descriptive statistics. In the qualitative study, acceptability was defined as "the perception that a given treatment is agreeable, palatable, or satisfactory." A thematic analysis was conducted using five dimensions of acceptability: content, complexity, comfort, delivery, and credibility. The mean age was 43.9 years (SD 4.4), and seven (58%) had primary education or less. While some participants reported feelings of uncertainty when they started using 5FU, at the end of the study, all 12 participants strongly agreed that the cream was safe and were confident they used it correctly. Most participants (91.7%) experienced no discomfort with the vaginal applicator, and most reported using tampons overnight after 5FU use, as recommended. Qualitative findings revealed that favorable perceptions of self-administered 5FU were driven by its ease of use, the discrete nature of the treatment, and the comfort of home application. The main challenges included correctly measuring the study drug, finding a private place at home to self-administer, and the need to use condoms during treatment. Compared to their previous ablation or excision treatments, participants found 5FU to be less painful, and all would prefer a self-administered treatment instead of a procedure if it were an option. Self-administered intravaginal 5FU as an adjuvant treatment for cervical precancer among women living with HIV in Kenya was highly acceptable. Randomized studies of 5FU and other topical therapies in LMICs are needed to evaluate their use in closing the current precancer treatment gaps in these settings.

Synchronization in spiking neural networks with short and long connections and time delays.

Synchronization is fundamental for information processing in oscillatory brain networks and is strongly affected by time delays via signal propagation along long fibers. Their effect, however, is less evident in spiking neural networks given the discrete nature of spikes. To bridge the gap between these different modeling approaches, we study the synchronization conditions, dynamics underlying synchronization, and the role of the delay of a two-dimensional network model composed of adaptive exponential integrate-and-fire neurons. Through parameter exploration of neuronal and network properties, we map the synchronization behavior as a function of unidirectional long-range connection and the microscopic network properties and demonstrate that the principal network behaviors comprise standing or traveling waves of activity and depend on noise strength, E/I balance, and voltage adaptation, which are modulated by the delay of the long-range connection. Our results show the interplay of micro- (single neuron properties), meso- (connectivity and composition of the neuronal network), and macroscopic (long-range connectivity) parameters for the emergent spatiotemporal activity of the brain.

A bio-lattice deep learning framework for modeling discrete biological materials.

Biological tissues dynamically adapt their mechanical properties at the microscale in response to stimuli, often governed by discrete interacting mechanisms that dictate the material's behavior at the macroscopic scale. An approach to model the discrete nature of these elemental units is the Lattice Spring Modeling (LSM). However, the interactions in biological matter can present a high degree of complexity and heterogeneity at the macroscale, posing a computational challenge in multiscale modeling. In this work, we propose a novel machine learning-based multiscale framework that integrates deep neural networks (DNNs), the finite element method (FEM), and a LSM-inspired microstructure description to investigate the behavior of discrete, spatially heterogeneous materials. We develop a versatile, assumption-free lattice framework for interacting discrete units, and derive a consistent multiscale connection with our FEM implementation. A single DNN is trained to learn the constitutive equations of various particle configurations and boundary conditions, enabling rapid response predictions of heterogeneous biological tissues. We demonstrate the effectiveness of our approach with extensive testing, starting with benchmark cases and progressively increasing the complexity of the microstructures. We explored materials ranging from soft to hard inclusions, then combined them to form a macroscopically homogeneous material, a gradient-varying polycrystalline solid, and fully randomized configurations. Our results show that the model accurately captures the material response across these spatially varying structures.

Guidelines for cerebrovascular segmentation: Managing imperfect annotations in the context of semi-supervised learning.

Segmentation in medical imaging is an essential and often preliminary task in the image processing chain, driving numerous efforts towards the design of robust segmentation algorithms. Supervised learning methods achieve excellent performances when fed with a sufficient amount of labeled data. However, such labels are typically highly time-consuming, error-prone and expensive to produce. Alternatively, semi-supervised learning approaches leverage both labeled and unlabeled data, and are very useful when only a small fraction of the dataset is labeled. They are particularly useful for cerebrovascular segmentation, given that labeling a single volume requires several hours for an expert. In addition to the challenge posed by insufficient annotations, there are concerns regarding annotation consistency. The task of annotating the cerebrovascular tree is inherently ambiguous. Due to the discrete nature of images, the borders and extremities of vessels are often unclear. Consequently, annotations heavily rely on the expert subjectivity and on the underlying clinical objective. These discrepancies significantly increase the complexity of the segmentation task for the model and consequently impair the results. Consequently, it becomes imperative to provide clinicians with precise guidelines to improve the annotation process and construct more uniform datasets. In this article, we investigate the data dependency of deep learning methods within the context of imperfect data and semi-supervised learning, for cerebrovascular segmentation. Specifically, this study compares various state-of-the-art semi-supervised methods based on unsupervised regularization and evaluates their performance in diverse quantity and quality data scenarios. Based on these experiments, we provide guidelines for the annotation and training of cerebrovascular segmentation models.

Structural Performance of Reinforced Concrete Beams with Steel Fibres as Secondary Reinforcement: Experimental and Pre-peak Numerical Modelling

This research consists of an experimental and a numerical investigation into improving the structural performance of reinforced concrete (RC) beams by incorporating hooked-end steel fibres. Experimentally, steel fibres were added to the concrete matrix of 80 mm × 180 mm × 1500 mm RC beams with fibre contents of 0%, 0.5%, and 1%, without replacing traditional reinforcement bars. Each beam underwent a four-point flexural test using a hydraulic press under static loading to evaluate the influence of fibres on flexural behaviour. The numerical analysis aimed to model the macro-scale flexural behaviour of RC beams using a 3D finite element approach in ABAQUS. Challenges in modelling steel fibres include their discrete nature and computational demands due to intricate meshing and convergence issues. The Simplified Concrete Damaged Plasticity Model was used to characterize the compressive and tensile behaviours of plain and steel fibre reinforced concretes. Nonlinear finite element analysis was performed to predict pre-peak load versus mid-span deflection and crack propagation. The model, which does not explicitly simulate steel fibres, was validated against experimental data, showing strong agreement and confirming its effectiveness in capturing the flexural behaviour of steel fibre reinforced concrete beams. Experiments Results showed that steel fibres enhance the flexural performance. Beams with steel fibres exhibited higher first crack loads, ultimate loads, flexural strengths, and toughness. Additionally, the fibres increased crack numbers, reduced crack spacing and length, and improved post-cracking behaviour. The simulation results were subsequently validated against experimental data. The results of the numerical finite element analysis were in good agreement with those of the experiments.

Stone Selection by Wild Chimpanzees Shares Patterns with Oldowan Hominins

The use of broad tool repertoires to increase dietary flexibility through extractive foraging behaviors is shared by humans and their closest living relatives (chimpanzees, Pan troglodytes). However, comparisons between tool use in ancient human ancestors (hominins) and chimpanzees are limited by differences in their toolkits. One feature shared by primate and hominin toolkits is rock selection based on physical properties of the stones and the targets of foraging behaviors. Here, we document the selectivity patterns of stone tools used by wild chimpanzees to crack nuts at Bossou, Guinea, through controlled experiments that introduce rocks unknown to this population. Experiments incorporate specific rock types because previous studies document hominin selection of these lithologies at Kanjera South 2 Ma. We investigate decisions made by chimpanzees when selecting stones that vary in their mechanical properties-features not directly visible to the individual. Results indicate that the selection of anvils and hammers is linked to task-specific mechanical properties. Chimpanzees select harder stones for hammers and softer stones for anvils, indicating an understanding of specific properties for distinct functions. Selectivity of rock types suggests that chimpanzees assess the appropriate materials for functions by discriminating these 'invisible' properties. Adults identify mechanical properties through individual learning, and juveniles often reused the tools selected by adults. Selection of specific rock types may be transmitted through the reuse of combinations of rocks. These patterns of stone selection parallel what is documented for Oldowan hominins. The processes identified in this experiment provide insights into the discrete nature of hominin rock selection patterns in Plio-Pleistocene stone artifact production.

Study on the Microstructure and Permeability Characteristics of Tailings Based on CT Scanning Technology

The permeability characteristics of tailings directly affect the position of the infiltration line of the tailings dam, which is the most critical factor affecting tailings dam failures. In order to fully analyze the essence of its permeability characteristics, computed tomography (CT) technology is used to analyze the structure of different types of tailings from a microscopic perspective and carry out microscopic seepage simulation. The results showed the following findings: (1) The porosity of viscous tailings ranges from 25 to 35%, the distribution of surface porosity along the height is relatively uniform, and the distribution is shown as having a certain discrete nature with the increase in particle size. (2) Compared with silty and sandy tailings, the surface of viscous tailings is smoother and more round, and the shape factor can reach 0.95; (3) The data gap between the simulation and the measurements by CT scanning technology is less than 10%, and the estimation of the permeability characteristics is feasible, with good applicability in the simulation of tailings seepage. (4) In the microscopic pore throat structure, the permeability characteristics of the tailings are more affected by the radius of the throat than the pore radius, and the exponential function relationship between the permeability coefficient and the porosity satisfies a high correlation. In this paper, the relationship between the microstructure and permeability characteristics of tailings is analyzed by CT technology; the permeability is simulated and calculated, and a permeability coefficient prediction model for tailings is proposed in combination with the experiment, which can provide a new idea and method for the study of the permeability characteristics of tailings.

The NANOGrav 15 yr Data Set: Looking for Signs of Discreteness in the Gravitational-wave Background

Abstract The cosmic merger history of supermassive black hole binaries (SMBHBs) is expected to produce a low-frequency gravitational wave background (GWB). Here we investigate how signs of the discrete nature of this GWB can manifest in pulsar timing arrays (PTAs) through excursions from, and breaks in, the expected f GW − 2 / 3 power law of the GWB strain spectrum. To do this, we create a semianalytic SMBHB population model, fit to North American Nanohertz Observatory for Gravitational Waves (NANOGrav’s) 15 yr GWB amplitude, and with 1000 realizations, we study the populations’ characteristic strain and residual spectra. Comparing our models to the NANOGrav 15 yr spectrum, we find two interesting excursions from the power law. The first, at 2 nHz, is below our GWB realizations with a p-value significance p = 0.05–0.06 (≈1.8σ–1.9σ). The second, at 16 nHz, is above our GWB realizations with p = 0.04–0.15 (≈1.4σ–2.1σ). We explore the properties of a loud SMBHB that could cause such an excursion. Our simulations also show that the expected number of SMBHBs decreases by 3 orders of magnitude, from ∼106 to ∼103, between 2 and 20 nHz. This causes a break in the strain spectrum as the stochasticity of the background breaks down at 26 − 19 + 28 nHz , consistent with predictions pre-dating GWB measurements. The diminished GWB signal from SMBHBs at frequencies above the 26 nHz break opens a window for PTAs to detect continuous GWs from individual SMBHBs or GWs from the early Universe.

INTEGRATION OF HIGH-TEMPERATURE SURFACE HEAT EXCHANGE

Despite the discrete nature of the interaction of droplets of sprayed liquid with a high-temperature surface, the inevitable formation of a liquid film leads to the fact that the main qualitative laws of the heat exchange that occurs in this case are characteristic of the known process of heat exchange during boiling. At the same time, the presence of extensive theoretical and experimental studies of “large-volume boiling” and the process of steam generation in channels does not allow us to establish the conditions of heat exchange during the thermal interaction of a dispersed liquid - water with different concentrations of surfactants with a high-temperature surface. We did not find any materials about this process in the scientific literature. With a number of features common to the above two cases of heat exchange (the presence of boiling crises, film and bubble modes, etc.), cooling a high-temperature surface with a droplet medium containing various concentrations of surfactants has significant distinctive features due to the hydrodynamics of the process, which is the subject of further study. To fully explore the above task, the following must be done: Develop a methodology for experimental study of local conditions of non-stationary heat exchange of sprayed water with different concentrations of surfactants. Develop and manufacture an experimental setup on which to conduct research on the effects of irrigation density, surface temperature, degree of liquid underheating, its speed and angle of impingement on the surface. Develop a mathematical model for calculations of heat flows, heat transfer coefficients, dynamics of hydraulic methods of liquid dispersion – water with different concentrations of surfactants, critical heat flows and surface temperatures in the transition region from film to bubble boiling as a function of determining factors. Establish the independent influence of the degree of non-stationarity of the process on the heat exchange conditions.

Parallelization of particle-based reaction-diffusion simulations using MPI.

Particle-based reaction-diffusion models offer a high-resolution alternative to the continuum reaction-diffusion approach, capturing the discrete and volume-excluding nature of molecules undergoing stochastic dynamics. These methods are thus uniquely capable of simulating explicit self-assembly of particles into higher-order structures like filaments, spherical cages, or heterogeneous macromolecular complexes, which are ubiquitous across living systems and in materials design. The disadvantage of these high-resolution methods is their increased computational cost. Here we present a parallel implementation of the particle-based NERDSS software using the Message Passing Interface (MPI) and spatial domain decomposition, achieving close to linear scaling for up to 96 processors in the largest simulation systems. The scalability of parallel NERDSS is evaluated for bimolecular reactions in 3D and 2D, for self-assembly of trimeric and hexameric complexes, and for protein lattice assembly from 3D to 2D, with all parallel test cases producing accurate solutions. We demonstrate how parallel efficiency depends on the system size, the reaction network, and the limiting timescales of the system, showing optimal scaling only for smaller assemblies with slower timescales. The formation of very large assemblies represents a challenge in evaluating reaction updates across processors, and here we restrict assembly sizes to below the spatial decomposition size. We provide the parallel NERDSS code open source, with detailed documentation for developers and extension to other particle-based reaction-diffusion software.

Granular metamaterials with dynamic bond reconfiguration.

Biological materials dynamically reconfigure their underlying structures in response to stimuli, achieving adaptability and multifunctionality. Conversely, mechanical metamaterials have fixed interunit connections that restrict adaptability and reconfiguration. This study introduces granular metamaterials composed of discrete bimaterial structured particles that transition between assembled and unassembled states through mechanical compression and thermal stimuli. These materials enable dynamic bond reconfiguration, allowing reversible bond breaking and formation, similar to natural systems. Leveraging their discrete nature, these materials can adaptively reconfigure their shape and respond dynamically to varying conditions. Our investigations reveal that these granular metamaterials can substantially alter their mechanical properties, like compression, shearing, and bending, offering tunable mechanical characteristics across different states. Furthermore, they exhibit collective behaviors like directional movement, object capture, transportation, and gap crossing, showcasing their potential for reprogrammable functionalities. This work highlights the dynamic reconfigurability and robust adaptability of granular metamaterials, expanding their potential in responsive architecture and autonomous robotics.

Breaking Through? The Divergent Consequences of CEO Political Ideology on Firm Inventiveness

We draw on upper-echelons literature recognizing the important role of CEOs in firm strategy, including innovation, and research on CEO political ideology and executive discretion to explore the relationship between CEO political ideology and firm breakthrough inventions. We suggest that CEO liberalism is a double-edged sword and is positively associated with firm breakthrough inventions but also less-useful inventions. We suggest that these relationships are shaped by three different sources of executive discretion: CEO-TMT pay gap, institutional investors, and existing product-market competition. We find support for our hypotheses on a sample of 581 public firms using firms’ patenting and citation activities to capture inventions. We infuse a values, contingency-based perspective to work on CEO characteristics and firm breakthrough inventions, provide a more comprehensive understanding of the multifaceted nature of managerial discretion in setting or deviating firms on/from certain technological trajectories, and extend work on political ideology by showing the relevance of political ideology for explaining not just variations in firm internal resource allocation decisions, corporate activism, and entrepreneurship but also in the level and nature of inventions it produces.

Compressive‐Sensing‐Assisted Mixed Integer Optimization for Dynamical System Discovery With Highly Noisy Data

ABSTRACTThe identification of governing equations for dynamical systems is an everlasting challenge for the fundamental research in science and engineering. Machine learning has exhibited great success in learn and predicting dynamical systems from data. However, the fundamental challenges still exist: discovering the exact governing equations from highly noisy data. In the present work, we propose a compressive sensing‐assisted mixed integer optimization (CS‐MIO) method to make a step forward from a modern discrete optimization lens. In particular, we first formulate the problem into a mixed integer optimization model. The discrete optimization nature of the model leads to exact variable selection by means of cardinality constraint, and thereby powerful capability of exact discovery of governing equations from noisy data. Such capability is further enhanced by incorporating compressive sensing and regularization techniques for highly noisy data and high‐dimensional problems. The case studies on classical dynamical systems have shown that CS‐MIO can discover the exact governing equations from large‐noise data, with up to two orders of magnitude larger noise compared with state‐of‐the‐art methods. We also show its effectiveness for high‐dimensional dynamical system identification through the chaotic Lorenz 96 system.

Modified genetic algorithm for efficient optimization: application to prestressed concrete structural elements

Prestressing, whether bonded or non-bonded, has become increasingly attractive as it allows for larger spans, greater architectural flexibility, greater agility in execution and greater durability. All of these advantages are capable of making prestressed concrete structures more economical solutions when associated with an efficient scaffolding system and formwork. The use of prestressed beams combined with ribbed slabs allows the slabs and beams to have the same height, thus favoring the architecture, in addition to facilitating the assembly of formwork and concreting. The designer can appropriately define the dimensions of the beams and slabs, as well as parameters such as the quantity and layout of cables by trial based on experience. However, the use of numerical optimization techniques are tools recognized as appropriate for the search for a structural solution, where design parameters, whether at the level of design (topology), intermediate (shape) or detail (dimension), can be determined such that the solution minimizes a performance measure, for example, cost, which takes into account the cost of materials, the volume of concrete, the weight of passive and active reinforcement, and labor productivity. Given the discrete nature of some variables, genetic algorithms (GA) have been widely used. GAs have control parameters and operators that are, in general, dependent on the problem, requiring calibrations in each case. The objective of this work is to review some optimization models and improve the GA of the BIOS program - developed at the (Computational Mechanics and Visualization Laboratory) LMCV of the Federal University of Ceará (UFC) - investigating new operators, thus aiming at the efficiency of the algorithm for prestressed elements, using a prestressed band beam as a case study. Genetic operators, in particular crossover, will be targets of the study.

Возможности искусственного интеллекта в характеристике очагов демилинизации пациентов с рассеянным склерозом

INTRODUCTION. Magnetic resonance imaging (MRI) of the central nervous system (CNS) is the method of choice in patients with clinical manifestations of multiple sclerosis (MS) to determine dissemination in space and time according to McDonald diagnostic criteria. Being an important diagnostic tool, tomography also acts as a method for further monitoring of the condition, including for assessing the subclinical course of the disease. The multiplicity of demyelination foci and the discrete nature of the course are an obstacle to a quick and accurate analysis of the volume of changes that have occurred. The automated system for analyzing MRI scans "Brain Snitch", developed by the Department of Nervous and Neurosurgical Diseases together with the Laboratory of Information and Computer Technologies of the Research Institute of the Belarusian State Medical University, allows tracking the current state of the lesion: the degree of activity of the pathological process, the size of the lesion, its localization and intensity. AIM. Evaluation of the state of absolute brightness of active demyelination foci in patients with MS using the automated system "Brain Snitch", based on the work of artificial intelligence. MATERIALS AND METHODS. Scans of various sequences of MRI studies (T1, T1 with contrast, T2, T2 FLAIR) of MRI studies of patients with MS. Active demyelination lesions visualized in T1 mode on MRI were compared with similar lesions in T2 and T2 FLAIR modes in the Brain Snitch program to obtain tabular values of absolute brightness (intensity equivalent, presented by the program using the artificial intelligence technology of the automated Brain Snitch program) of these lesions. RESULTS AND DISCUSSION. Using the automated Brain Snitch program based on artificial intelligence technology, a tendency towards reduced absolute brightness of active demyelination lesions (p = 0.046) was found, compared with the absolute brightness values in T2 mode of lesions similar to inactive (not accumulating contrast agent) in T1 mode. CONCLUSIONS. The tendency found in the course of the study to a reduced absolute brightness of active foci in the T2 mode may serve as a basis for the development of additional criteria for evaluating the activity of pathomorphological foci in multiple sclerosis in radiological and neurological practice.