Computational Model Research Articles

The Effect of Cranial Sutures Should Be Considered in Transcranial Electrical Stimulation.

Computational modeling is used to optimize transcranial electrical stimulation (tES) approaches, and the precision of these models is dependent on their anatomical accuracy. We are unaware of any computational modeling of tES that has included cranial sutures. The aims of the study were to review the literature on the timing of closure of the coronal and squamous sutures, which are situated under electrode placements used in tES; to review the literature regarding differences in skull and suture conductivity and to determine a more accurate conductivity for sutures; and to identify magnetic resonance image (MRI) techniques that could be used to detect cranial sutures. A scoping review of medical literature was conducted. We conducted computational modeling of a cranial bone plug using COMSOL Multiphysics finite element software, utilizing methodology and results from a previous study. We assessed use of the "3D Slicer" software to identify sutures in routine T1-weighted MRI scans. Reports from forensic examinations and computed tomography (CT) scans showed suture closure does not correlate with age. Our computational modeling determined a cranial suture conductivity of 0.32 S/m, which is much higher than for skull (compact skull 0.004 S/m, standard trilayer 0.013 S/m). 3D slicer enabled rapid and precise identification of the anatomy and location of cranial sutures. Cranial sutures persist throughout the lifespan and have a far higher conductivity than skull bone. Cranial sutures can be localized quickly and precisely using a combination of MRI and readily available modeling software. Sutures should be included in tES computational modeling and electroencephalography source imaging to improve the accuracy of results.

The effective multiplicity of infection for HCMV depends on the activity of the cellular 20S proteasome.

Human cytomegalovirus (HCMV) is a betaherpesvirus capable of infecting numerous cell types and persisting throughout an infected individual's life. Disease usually occurs in individuals with compromised or underdeveloped immune systems. Several antivirals exist but have limitations relating to toxicity and resistance. HCMV replication involves upregulation of host proteasomal activities, which play important roles in the temporal stages of replication. Here, we defined the impact on replication kinetics of the proteasome inhibitor, bortezomib. We demonstrate that bortezomib significantly reduces levels of viral genomes and infectious virions produced from a population of cells. Inhibition reduced expression of viral proteins that are influenced by genome synthesis. When added prior to 24 hpi, we observe decreases in PCNA and Cdk1 while increases in p21 whose regulations contribute to efficient replication. This response synergized with an antiviral, maribavir. Since some replication occurred, we tested the hypothesis that a subset of infected cells might break through inhibition. Initially, we simulated bortezomib activities using a mechanistic computational model of late-lytic replication. Upon reducing multiplicity of infection (MOI) in silico, we observed near-identical simulated results compared to experimental data. Next, we analyzed replication using live-cell imaging. This revealed treated cultures do contain a population of cells with fully developed late-stage cytoplasmic assembly compartments but at significantly lower numbers. We refer to this as the effective MOI. Overall, our studies support a hypothesis in which 20S proteasome inhibition disrupts HCMV replication by reducing the MOI to an effective MOI, defined by a fraction of infected cells capable of progressing to fulminant infection.IMPORTANCEHuman cytomegalovirus (HCMV) infection and reactivation continues to contribute to morbidity and mortality around the world. Antiviral compounds are available but have limitations. Here, we have defined the impact of the proteasome inhibitor bortezomib on HCMV replication. Proteasomal activities play a critical role in temporal changes required for replication. We demonstrate that disrupting these activities inhibits viral replication while likely supporting increased antiviral activity of the anti-HCMV agent, maribavir. Using a combination of live-cell imaging and computational tools, we discover that a subset of infected cells progresses to fulminant infection, which we define as the effective multiplicity of infection, and this subset would otherwise be missed when analyzing the average of the population.

Multi-regional control of amygdalar dynamics reliably reflects fear memory age

The basolateral amygdala (BLA) is crucial for the encoding and expression of fear memory, yet it remains unexplored how neural activity in this region is dynamically influenced by distributed circuits across the brain to facilitate expression of fear memory of different ages. Using longitudinal multisite electrophysiological recordings in male mice, we find that the recall of older contextual fear memory is accompanied by weaker, yet more rhythmic, BLA gamma activity which is distally entrained by theta oscillations in both the hippocampal CA1 and the anterior cingulate cortex. Computational modeling with Light Gradient Boosting Machine using extracted oscillatory features from these three regions, as well as with Transformer using raw local field potentials, accurately classified remote from recent memory recall primarily based on BLA gamma and CA1 theta. These results demonstrate in a non-biased manner that multi-regional control of BLA activity serves as reliable neural signatures for memory age-dependent recall mechanisms.

Infinite-Dimensional Quantum Entropy: The Unified Entropy Case

Infinite-dimensional systems play an important role in the continuous-variable quantum computation model, which can compete with a more standard approach based on qubit and quantum circuit computation models. But, in many cases, the value of entropy unfortunately cannot be easily computed for states originating from an infinite-dimensional Hilbert space. Therefore, in this article, the unified quantum entropy (which extends the standard von Neumann entropy) notion is extended to the case of infinite-dimensional systems by using the Fredholm determinant theory. Some of the known (in the finite-dimensional case) basic properties of the introduced unified entropies were extended to this case study. Certain numerical examples for computing the proposed finite- and infinite-dimensional entropies are outlined as well, which allowed us to calculate the entropy values for infinite Hilbert spaces.

HybridGNN: A Self-Supervised Graph Neural Network for Efficient Maximum Matching in Bipartite Graphs

Solving maximum matching problems in bipartite graphs is critical in fields such as computational biology and social network analysis. This study introduces HybridGNN, a novel Graph Neural Network model designed to efficiently address complex matching problems at scale. HybridGNN leverages a combination of Graph Attention Networks (GATv2), Graph SAGE (SAGEConv), and Graph Isomorphism Networks (GIN) layers to enhance computational efficiency and model performance. Through extensive ablation experiments, we identify that while the SAGEConv layer demonstrates suboptimal performance in terms of accuracy and F1-score, configurations incorporating GATv2 and GIN layers show significant improvements. Specifically, in six-layer GNN architectures, the combinations of GATv2 and GIN layers with ratios of 4:2 and 5:1 yield superior accuracy and F1-score. Therefore, we name these GNN configurations HybridGNN1 and HybridGNN2. Additionally, techniques such as mixed precision training, gradient accumulation, and Jumping Knowledge networks are integrated to further optimize performance. Evaluations on an email communication dataset reveal that HybridGNNs outperform traditional algorithms such as the Hopcroft–Karp algorithm, the Hungarian algorithm, and the Blossom/Edmonds’ algorithm, particularly for large and complex graphs. These findings highlight HybridGNN’s robust capability to solve maximum matching problems in bipartite graphs, making it a powerful tool for analyzing large-scale and intricate graph data. Furthermore, our study aligns with the goals of the Symmetry and Asymmetry Study in Graph Theory special issue by exploring the role of symmetry in bipartite graph structures. By leveraging GNNs, we address the challenges related to symmetry and asymmetry in graph properties, thereby improving the reliability and fault tolerance of complex networks.

Backdoor Attack Against Dataset Distillation in Natural Language Processing

Dataset distillation has become an important technique for enhancing the efficiency of data when training machine learning models. It finds extensive applications across various fields, including computer vision (CV) and natural language processing (NLP). However, it essentially consists of a deep neural network (DNN) model, which remain susceptible to security and privacy vulnerabilities (e.g., backdoor attacks). Existing studies have primarily focused on optimizing the balance between computational efficiency and model performance, overlooking the accompanying security and privacy risks. This study presents the first backdoor attack targeting NLP models trained on distilled datasets. We introduce malicious triggers into synthetic data during the distillation phase to execute a backdoor attack on downstream models trained with these data. We employ several widely used datasets to assess how different architectures and dataset distillation techniques withstand our attack. The experimental findings reveal that the attack achieves strong performance with a high (above 0.9 and up to 1.0) attack success rate (ASR) in most cases. For backdoor attacks, high attack performance often comes at the cost of reduced model utility. Our attack maintains high ASR while maximizing the preservation of downstream model utility, as evidenced by results showing that the clean test accuracy (CTA) of the backdoored model is very close to that of the clean model. Additionally, we performed comprehensive ablation studies to identify key factors affecting attack performance. We tested our attack method against five defense strategies, including NAD, Neural Cleanse, ONION, SCPD, and RAP. The experimental results show that these defense methods are unable to reduce the attack success rate without compromising the model’s performance on normal tasks. Therefore, these methods cannot effectively defend against our attack.

A boundedly rational model for category learning

The computational modeling of category learning is typically evaluated in terms of the model's accuracy. For a model to accurately infer category membership of stimuli, it has to have sufficient representational precision. Thus, many category learning models infer category representations that guide decision-making and the model's fitness is evaluated by its ability to accurately choose. Substantial decision-making research, however, indicates that noise plays an important role. Specifically, noisy representations are assumed to introduce an element of stochasticity to decision-making. Noise can be minimized at the cost of cognitive resource expenditure. Thus, a more biologically plausible model of category learning should balance representational precision with costs. Here, we tested an autoencoder model that learns categories (the six category structures introduced by Roger Shepard and colleagues) by balancing the minimization of error with minimization of resource usage. By incorporating the goal of reducing category complexity, the currently proposed model biases category decisions toward previously learned central tendencies. We show that this model is still able to account for category learning performance in a traditional category learning benchmark. The currently proposed model additionally makes some novel predictions about category learning that future studies can test empirically. The goal of this paper is to make progress toward development of an ecologically and neurobiologically plausible model of category learning that can guide future studies and theoretical frameworks.

Intracortical recordings reveal the neuronal selectivity for bodies and body parts in the human visual cortex

Body perception plays a fundamental role in social cognition. Yet, the neural mechanisms underlying this process in humans remain elusive given the spatiotemporal constraints of functional imaging. Here, we present intracortical recordings of single- and multiunit spiking activity in two epilepsy surgery patients in or near the extrastriate body area, a critical region for body perception. Our recordings revealed a strong preference for human bodies over a large range of control stimuli. Notably, body selectivity was driven by a distinct selectivity for body parts. The observed body selectivity generalized to nonphotographic depictions of bodies including silhouettes and stick figures. Overall, our study provides unique neural data that bridge the gap between human neuroimaging and macaque electrophysiology studies, laying a solid foundation for computational models of human body processing.

Numerical investigation of the bridging and current flow of a positive DC streamer using a 1.5D model

Abstract Single streamers, at nanosecond-timescales, can be simulated using detailed computational models with a high-dimensional representation. These models are computationally impractical for parametric explorations and simulation of longer times, that can follow many-streamer pulsations and the influence of one streamer burst on the next. This work develops a 1.5D model of a positive DC streamer for simulations beyond the electrode-gap bridging phase, and uses it to parametrically explore the impact of different terms and operational parameters. The geometry of interest is that of a tip-to-plane electrode configuration under DC voltage, and the simulation is followed for the duration of one current pulse (order 500 ns). The numerical model uses an axisymmetric boundary element method to solve for the electric field, as well as a ‘stack’ of 3 different transient solvers to improve efficiency and allow solving over longer timescales. The model is able to resolve the development of the cathode sheath during the streamer bridging phase using a kinetic flux boundary condition. It also gives qualitative agreement to current waveforms using an equivalent experimental setup. The different phases of the current pulse (streamer propagation, bridging, and current-flow phase) are discussed in detail.

State preparation by shallow circuits using feed forward

Fault tolerant quantum computers repetitively apply a four-step procedure: First, perform a few one and two-qubit quantum gates. Second, perform a syndrome measurement on a subset of the qubits. Third, perform fast classical computations to establish if and where errors occurred. And, fourth, correct the errors with a correction step. The next iteration applies the same procedure with new one and two-qubit gates. Even though current error-rates prohibit this procedure to work and fault tolerant quantum computing remains a distant goal, the same procedure can already prove useful today. In this work we make use of this four-step scheme not to carry out fault-tolerant computations, but to enhance short, constant-depth, quantum circuits that perform 1 qubit gates and nearest−neighbor 2 qubit gates.We introduce a new computational model called Local Alternating Quantum Classical Computations(LAQCC). In this model, qubits are placed in a grid and they can only interact with their direct neighbors; the quantum circuits are of constant depth with intermediate measurements; a classical controller can perform log-depth computations on these intermediate measurement outcomes and control future quantum operations based on the outcome. This model fits naturally between quantum algorithms in the NISQ era and full-fledged fault-tolerant quantum computation. We first prove that any Clifford circuit has an equivalent LAQCC circuit, and that any LAQCC circuit can be simulated by a QNC1circuit. Next, we conjecture the non-simulatability of LAQCC by showing that LAQCC contains the class of Instantaneous Quantum Polynomial-time circuits. We also show that any LAQCC circuit with polynomial-sized quantum circuits and unbounded classical computations is contained in the class of quantum circuits equipped with post-selection gates with respect to the task of state preparation. We continue by presenting LAQCC implementations of different subroutines, including OR-gates, quantum Fourier transforms and Threshold gates. These subroutines prove vital in constructing three state preparation routines in the main part of this work. Preparing a uniform superposition uses constant-depth arithmetic gates, combined with an exact Grover implementation by Long. For the W-state, we employ a compress-uncompress method to switch between a binary and one-hot encoding. This method extends to the more generalized Dicke-states, the superposition of n-bit strings of Hamming weight k, for k=O(n), but fails for higher k due to the birthday paradox. We extend this protocol to a protocol that prepares many-body scar states, highly excited states with low entanglement and longer coherence times than states with the same energy density. We present a circuit for preparing Dicke-states for larger k requiring log-depth circuits that maps between the factoradic number system and the combinatorial number system.

A Graph-Based Machine-Learning Approach Combined with Optical Measurements to Understand Beating Dynamics of Cardiomyocytes.

The development of computational models for the prediction of cardiac cellular dynamics remains a challenge due to the lack of first-principled mathematical models. We develop a novel machine-learning approach hybridizing physics simulation and graph networks to deliver robust predictions of cardiomyocyte dynamics. Embedded with inductive physical priors, the proposed constraint-based interaction neural projection (CINP) algorithm can uncover hidden physical constraints from sparse image data on a small set of beating cardiac cells and provide robust predictions for heterogenous large-scale cell sets. We also implement an in vitro culture and imaging platform for cellular motion and calcium transient analysis to validate the model. We showcase our model's efficacy by predicting complex organoid cellular behaviors in both in silico and in vitro settings.

Abstract A001: Navigating the future of therapeutic development: innovations in safety and efficacy through advanced research methodologies and nanotechnology

Abstract In recent years, the field of therapeutic development has experienced a significant paradigm shift driven by strategic scientific innovations that prioritize safety and efficacy in therapeutic agents. This paper explores the intersection of advanced research methodologies, novel material sciences, and biotechnological advancements that are revolutionizing the approach to drug formulation and delivery systems. The integration of precision medicine principles alongside cutting-edge technologies allows for the design of therapeutics that are not only effective in targeting specific diseases but are also engineered to minimize adverse effects associated with conventional therapies. One focal point of this exploration is the application of nanotechnology in drug delivery systems. By utilizing nanoparticles, researchers can enhance the bioavailability of therapeutic agents while reducing systemic toxicity. This targeted approach enables the delivery of higher concentrations of drugs to the affected areas, thereby maximizing therapeutic efficacy and minimizing side effects. Additionally, the paper highlights the role of computational modeling and simulations in predicting the interaction of therapeutic agents at the molecular level, facilitating the identification of safer compounds through virtual screening methods. Another significant aspect addressed in this paper is the development of biologics and biosimilars as safer alternatives to traditional synthetic drugs. The advancement of biomanufacturing processes has made it feasible to produce complex molecules with high specificity and reduced immunogenicity, paving the way for innovative therapies that cater to individual patient profiles. Furthermore, the strategic incorporation of pharmacogenomics in drug development allows for the customization of therapeutic agents, ensuring that treatment regimens are tailored to the genetic makeup of patients, which can drastically improve safety and treatment outcomes. The paper also discusses the regulatory landscape surrounding the introduction of these innovative therapeutic agents. It emphasizes the need for adaptive regulatory frameworks that keep pace with scientific advancements while ensuring patient safety and efficacy. Collaborative efforts between researchers, regulatory bodies, and pharmaceutical companies are essential to facilitate the translation of innovative therapies from the laboratory to clinical practice. In conclusion, this paper underscores the critical importance of strategic scientific innovations in the quest for safer therapeutic agents. By harnessing the potential of advanced technologies, researchers can develop therapeutics that not only meet the growing demand for efficacy but also uphold the highest standards of safety, ultimately improving patient care and health outcomes. The findings presented advocate for continued investment in research and collaboration across disciplines to drive future advancements in therapeutic development. Citation Format: Peter O. David. Navigating the future of therapeutic development: innovations in safety and efficacy through advanced research methodologies and nanotechnology. [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Optimizing Therapeutic Efficacy and Tolerability through Cancer Chemistry; 2024 Dec 9-11; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(12_Suppl):Abstract nr A001

Eulerian Parameter Inference: a probabilistic change of variables for model-based inference with high-variability data sets

Advances in measurement technology have led to the generation of increasingly large data sets across various scientific fields. Data that capture the variability of the underlying system or process, such as single-cell or imaging data, are particularly interesting. However, calibrating computational models to explain this type of data remains challenging.We interpret the model calibration as a Stochastic Inverse Problem (SIP), where the measurements are interpreted as probabilistic samples. Our new SIP solution approach, Eulerian Parameter Inference (EPI), only requires data with large empirical variance and a deterministic simulation model that maps model parameters to simulation results. EPI solves the SIP through a change of variables. This computationally efficient approach allows for direct and point-wise evaluation of parameter densities without ever inverting the simulation model. The resulting parameter distribution captures data variability completely, enabling full data reconstruction. Further, estimating multivariate model parameters can be reduced to a series of one-dimensional problems under the assumption of stochastically independent parameters. This property is vital for complex simulation models and involved problems.With EPI, we explain 1) annual average temperature data of 3168 weather stations around the world with an arithmetic model, 2) German district-specific COVID-19 infection data through a differential equation model, and 3) artificial data generated via a partial differential equation model with independent parameters. With a robust mathematical foundation and an easily accessible Python package, we provide a versatile solution framework for integrating data with a high variance into simulation models that promotes application to various research questions.

Impacts of Re-export Demand on Benin’s Economy

This article analyses the impact of re-export demand on the Beninese economy. Re-export is dominant in Benin’s trade and may represent risks to the economy’s vulnerability. The methodology uses a dynamic computable general equilibrium model. It simulates two scenarios: the first considers a one-off decline in re-export demand in the first year, while the second analyses the impact of a recurring annual shock. The results, both at the aggregate and sectoral levels, indicate that the decline in re-export demand would negatively affect real GDP, domestic and net external demand, and most industries. This article extends the existing knowledge on the re-export phenomenon by adding a dynamic analysis of its potential impact at the aggregate and sectoral levels. The results confirm that re-export is a source of vulnerability for the Beninese economy. The authorities should focus on diversifying the economy and strengthening the competitiveness of the Port of Cotonou, in order to mitigate vulnerabilities linked to re-export activities. JEL Codes: C68, F17

Machine-Learning-Based Spectral Modeling: A Biomimetic Guide for Enhancing Esthetics.

To provide guidelines and means for optimal coverage and distribution of computer models with 1-10 clusters, designed based on an invivo extensive dental colorimetric database and compare the findings with some reputable shade guides. The Fuzzy C-Means (FCM) algorithm was used to optimize the tab position in the CIELAB color space, while MATLAB Fuzzy Logic was used to implement the codes. A minimization process was performed to fine-tune the cluster centers, minimizing Coverage Error (CE00) and Maximum Error (ME00). Spectral reconstruction was performed, and the results were compared with the corresponding data for VITA classical A1-D4 (VC), Linearguide 3D-Master (LG), and Bleachedguide 3D-Master (BG) shade guide. Paired t-test was employed to assess the significance of CE00 differences. CE00 and ME00 ranges for 1-to-10-tab models were 3.8 (14.2) to 1.7 (4.3), while the corresponding values for shade guides were 3.1 (9.1) for VC, 2.3 (6.0) for LG, and 2.9 (9.3) for BG. Hence, the 4-tab-model with CE00 of 2.2 and ME00 of 7.1, outperformed CE00 of LG (the lowest/best CE00 among shade guides), while the 6-tab-model, with CE00 of 2.0 and ME00 of 5.6, outperformed its ME00. A paired t-test confirmed that increasing the number of tabs models resulted in significant CE improvement in all cases (p ≤ 0.05). Shade guide models with only 4 to 6 tabs, optimized using computational techniques, outperformed physical dental shade guides tested, exhibiting significantly lower (better) Coverage Error and Maximum Error. Given that the spectral modeling was performed on an extensive invivo database of natural teeth, this approach was validated as a biomimetic guide for shade guides and corresponding materials of the future, offering a possibility for better shade matching with fewer shades/shade tabs.

3D Computational Modeling of Blast Transmission through the Fluid-Filled Cochlea and Hair Cells.

Veterans commonly suffer from blast-induced hearing disabilities. Injury to the sensitive organ of Corti (OC) or hair cells within the cochlea can directly lead to hearing loss, but is very difficult to measure experimentally. Computational finite element (FE) models of the human ear have been used to predict blast wave transmission through the middle ear and cochlea, but these models lack a representation of the OC. This paper reports a recently developed 3D FE model of the OC to simulate the response of hair cells to blast waves and predict possible injury locations. Components of the OC model consist of the sensory cells, membranes, and supporting cells with endolymphatic fluid surrounding them inside the scala media. Displacement of the basilar membrane induced by a 31-kPa blast overpressure derived from the macroscale model of the human ear was applied as input to the OC model. The fluid-structure interaction coupled analysis in the time domain was conducted in ANSYS. Major results derived from the FE model include the strains and displacements of the outer hair cells, stereociliary hair bundles (HBs), reticular lamina, and the tectorial membrane (TcM). The highest structural strain was concentrated around the connecting region of the HBs and the TcM, potentially indicating detachment due to blast exposure. Including the interstitial fluid in the OC created a realistic environment and improved the accuracy of the results compared to the previously published OC model without fluid. The microscale model of OC was developed in order to simulate blast overpressure transmission through the fluid-filled cochlea and hair cells. This FE model represents a significant advancement in the study of blast wave transmission through the inner ear, and is an important step toward a comprehensive multi-scale model of the human ear that can predict blast-induced injury and hearing loss.

Hydrothermal Stability of Active Sites in Cu-Exchanged Small-Pore Zeolites for the Selective Catalytic Reduction of NOx.

Combining operando X-ray absorption spectroscopy (XAS) and computational modelling shows unequivocally the distribution of active species in fresh and hydrothermally aged Cu-CHA and Cu-AEI zeolites during NH3-assisted selective catalytic reduction of NOx. Four principal species co-exist: (i) CuI cations coordinated to NH3, (ii) CuI cations coordinated to the zeolite framework, (iii) solvated CuII cations, and (iv) framework-coordinated CuII species (CuII st) formed upon hydrothermal ageing of the zeolite sample. The CuII st species were only observed in the hydrothermally aged zeolite samples and are formed upon the interaction of hydrated CuII cations with extra-framework Al (EFAl) generated during the hydrothermal treatment. These sites are inactive for NOx reduction, leading to a decrease in the catalytic performance of the hydrothermally aged zeolites. CuII st formation was higher in Cu-CHA (~46 %) than in Cu-AEI (~28 %). The better hydrothermal stability of Cu in the AEI framework is attributed to the tortuous channel structure of AEI that hinders the migration of hydrated CuII cations during hydrothermal ageing.

ADDITIVE TECHNOLOGIES IN CONSTRUCTION: TECHNICAL, ECONOMIC AND MANAGEMENT ANALYSIS

The study is devoted to a comprehensive analysis of the introduction of additive technologies into modern construction production, revealing the technical, economic, and managerial aspects of concrete 3D-printing of architectural structures. The work systematically analyzes the evolution of additive manufacturing technologies and identifies the main structural types of 3D-printers (an additive machine for layer-by-layer construction of objects), including portal, robotic, mobile, and hybrid systems. A detailed study of the technological parameters of concrete 3D-printing with concrete mixtures is presented, in particular, optimal printing speed modes, layer parameters, criteria for shaping and quality of building structures. A comparative analysis of the technological capabilities of leading world equipment manufacturers, such as ICON, COBOD International, Apis Cor, and WinSun, is conducted. The economic analysis demonstrates significant advantages of additive technologies: reduction of construction time by 2-6 times, reduction of construction cost by 20-35%, and increase in the load-bearing capacity of structures. The study comprehensively explores the structure of capital and operational expenses associated with technology implementation. Special emphasis is placed on the management aspects of introducing additive technologies, highlighting the critical need for an interdisciplinary approach and knowledge integration across architecture, engineering, management, and computer modeling. The study determines the prospects for the development of concrete 3D-printing technology in the construction industry and outlines the main directions of further scientific research and practical implementation of innovative solutions. This study was developed between the Department of "Integrated Technologies of Mechanical Engineering" named after M. Semko of NTU "KhPI" and "Geopolimer" LTD to implement innovative technologies in the construction industry.

DREAM GENERATION USING AI: COMPARISON OF ALGORITHMS

Dream generation is an emerging field within artificial intelligence that aims to replicate the human experience of dreaming through computational models. This paper compares various AI algorithms used for dream generation, evaluating their performance, creativity, and computational efficiency. We explore Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer-based models, providing a comprehensive analysis of their strengths and weaknesses. Our results indicate that each model has unique advantages, suggesting potential hybrid approaches for future research.

A Fast Modeling Method for BOR–FDTD

Aiming at the inefficiency caused by the optimal design of rotationally symmetric horn feed models, a fast modeling method for rotationally symmetric structures is proposed, which is used to deal with the mesh generation of rotationally symmetric structures and the rapid establishment of computational models. In this paper, the body-of-revolution finite-difference time-domain (BOR–FDTD) method is employed to investigate the radiation performance of the horn feed. Due to the rotational symmetry of the horn antenna, modeling only requires the establishment of a two-dimensional cross-sectional mesh of the horn feed. An optimized Delaunay triangulation algorithm combined with the projection intersection method is utilized to triangulate the horn cross-section of arbitrary polygons and establish the BOR–FDTD computational mesh. Results from both single-medium and multi-medium triangulation algorithms and computational models verify the accuracy of this modeling method. The radiation patterns of a smooth-walled horn were calculated and compared with the modeling time of MATLAB 2017 and the simulation time of CST. The results reveal that the algorithm presented in this paper aligns well with the simulation results from CST; furthermore, the modeling time amounts to only 6.78% of the MATLAB program’s modeling time, while the total simulation time is 31.3% of CST, which demonstrates both the accuracy and efficiency of the proposed method.