Computational Model Research Articles

COMPUTER SIMULATION OF ELECTRICALLY CONDUCTING PROPERTIES OF MATERIAL FOR PERSONAL PROTECTION EQUIPMENT

The article presents an analysis of production factors that have a harmful effect on the health of workers. It has been established that one of the leading factors is an increased electrostatic field that occurs when a static charge accumulates on the surface of materials. An increased level of the electrostatic field can lead to various negative consequences for workers’ health. For full antistatic protection at work, not only special tools and workplaces are needed, but also special footwear made of conductive materials with high surface resistance, as well as other personal protective equipment. A computer model for simulating the occurrence and development of an electrical breakdown in a protective material for personal protective equipment is described. The model is based on energy concepts of the occurrence and development of a discharge, taking into account the statistical heterogeneity of the material’s electrical conductivity. The process of breakdown development is considered as a special case of the percolation effect and a branching random process. The model makes it possible to find a relationship between the heterogeneity of the material and the threshold of its protective properties.

A normative framework dissociates need and motivation in hypothalamic neurons.

Physiological needs evoke motivational drives that produce natural behaviors for survival. In previous studies, the temporally intertwined dynamics of need and motivation have made it challenging to differentiate these two components. On the basis of classic homeostatic theories, we established a normative framework to derive computational models for need-encoding and motivation-encoding neurons. By combining the model-based predictions and naturalistic experimental paradigms, we demonstrated that agouti-related peptide (AgRP) and lateral hypothalamic leptin receptor (LHLepR) neuronal activities encode need and motivation, respectively. Our model further explains the difference in the dynamics of appetitive behaviors induced by optogenetic stimulation of AgRP or LHLepR neurons. Our study provides a normative modeling framework that explains how hypothalamic neurons separately encode need and motivation in the mammalian brain.

Influence of natural hyoid bone position and surgical repositioning on upper airway patency: A computational finite element modeling study

The hyoid bone's inferior baseline position in obstructive sleep apnea (OSA) has led to surgical hyoid repositioning (SHR) treatment, yet outcomes vary widely. The influence of baseline hyoid position (BHP; phenotype) and SHR on upper airway (UA) function remains unclear. We aimed to investigate their impact on the UA using computational modeling. Methods: A validated finite element model of the rabbit UA was advanced and used to simulate changes in BHP and SHR, alone and in combination. The hyoid was displaced in cranial, caudal, anterior, anterior-cranial and anterior-caudal directions from 1-4mm. Model outcomes included UA collapsibility, measured using closing pressure (Pclose), cross-sectional area (CSA) and soft tissue mechanics (displacement, stress and strain). Results: Graded BHP increments increased Pclose for all directions, and up to 29-43% at 4mm (relative to the original BHP). Anterior-based SHR decreased Pclose (~-115% at 4mm) and increased ΔCSA (~+35% at 4mm). Cranial SHR decreased ΔPclose (-29%), minimally affecting CSA. Caudal SHR increased ΔPclose (+27%) and decreased ΔCSA (-7%). Anterior-cranial and anterior-caudal SHR produced the highest stresses and strains. SHR effects on UA outcomes were dependent on BHP, with more caudal BHPs leading to less effective surgeries. Conclusion: BHP (phenotype) and SHR both alter UA outcomes, with effects dependent on hyoid displacement direction and magnitude. BHP influences the effectiveness of SHR in reducing UA collapsibility. These findings provide further insights into the hyoid's role in UA patency and suggest that considering the hyoid's baseline position and surgical repositioning direction/increment may help improve hyoid surgeries for OSA treatment.

Multifunctional Organometallic Compounds: An Interesting Luminescent NLO‐Active Alkynylplatinum (II) Complex

AbstractReaction of 4‐methylphenylacetylide with the known chlorido platinum(II) complex bearing the cyclometalated 1,3‐bis(pyridin‐2‐yl)‐4,6‐difluoro‐benzene ligand afforded a novel alkynyl platinum(II) complex, 1, that was fully characterized by elemental analysis, NMR and UV‐visible spectroscopies, by photoluminescence measurements, and by computational modelling. Its structure was determined by X‐ray diffraction studies on a single crystal whereas its second‐order nonlinear optical (NLO) properties were determined in solution by the Electric‐Field Induced Second Harmonic generation method. The novel multifunctional complex 1 is characterized by good luminescence properties, like the related chlorido Pt(II) complex, but by a much higher second‐order NLO response.

Optimizing Fish Feed Pellets Handling Systems to Minimize Microplastics Emissions - A Pilot Test Based Approach

The fisheries and aquaculture industries are vital components of global food production, yet they also contribute to release of microplastics into marine environments, posing risks to ecosystem health and seafood safety. Among the contributing factors, the erosion of feeding pipes during the pneumatic conveyance of fish feed pellets stands out as a significant source of microplastic pollution. This study focuses on optimizing feed pellet conveying systems in fish farms to minimize microplastic emissions while maximizing pipeline lifespan and pellet integrity. Using high-density polyethylene (HDPE) pipes commonly found in aquaculture facilities, the impact of air velocity and pipeline configuration on pipe wall erosion and pellet breakage was investigated. Through pneumatic conveying tests, the effects of varying air flow rates and bend radii were assessed on pipeline wear and feed pellet integrity. The findings of the study underscored the importance of optimizing operating parameters to bring a balance between preventing pipe blockages and minimizing abrasive impacts on pellets and pipeline surfaces. Furthermore, a simulationbased approach to optimize feeding system performance is presented, integrating the experimental results into a computational model. This model allows for the evaluation of different operating conditions and pipeline configurations, offering insights into costeffective strategies for reducing microplastic emissions while maintaining efficient feed delivery to fish populations. Ultimately, this research provided practical recommendations and best practices for the aquaculture industry to mitigate microplastic pollution from feeding pipes. By optimizing feed pellet conveying systems, environmental sustainability can be enhanced, seafood quality be preserved, and bolster consumer confidence in aquaculture products.

Local Morphologic and Hemodynamic Analyses for the Prediction of Abdominal Aortic Aneurysm Rupture Based on Patient-Specific CTA and Computational Modeling.

Extensive research has focused on the evaluation of rupture risks in abdominal aortic aneurysms (AAAs) through comprehensive morphologic and hemodynamic analyses, primarily considering the AAA as a whole entity. This study tried to identify the high-risk rupture sites of AAAs more precisely before the fatal process based on morphologic and hemodynamic analyses at the local segment. Computed tomography angiography of a specific AAA patient was conducted at the follow-up 4 months before rupture, 1 day before rupture, the day of the rupture, and 15 days after endovascular aortic repair. The evolution of local morphology and the hemodynamic characteristics at these critical timepoints were investigated based on patient-specific reconstructions and computational fluid dynamics. The morphologic and hemodynamic parameters of the rupture region vary continuously in the process of AAA development and rupture. The surface area and volume of the rupture segment were gradually enlarged at the follow-up 4 months before rupture (47.33 cm2; 67.35 mL), 1 day before rupture (57.23 cm2; 85.24 mL), and on the day of the rupture (62.41cm2; 104.73ml). A prominent decrease in time-averaged wall shear stress and velocity for the rupture segment is observed. The percentages of the lowest time-averaged wall shear stress (<0.1 Pa) area are increased in the AAA region (20.42%, 33.85%, and 53.00%, separately). The results based on precisely rebuilt geometries for the complete follow-ups of patient-specific computed tomography angiography demonstrate that notable morphologic and hemodynamic evolutions have occurred in the local segment of the AAA, which was further proved at the rupture site. The significant changes occurring at the local segment may provide valuable information for the evaluation of aneurysm rupture risk and locate the most probable site of rupture. Capturing the entire process of AAA rupture through CTA imaging is a rare occurrence in clinical practice. The evolution of morphology and hemodynamic characteristics observed in the illustrated results provides valuable insights for clinicians to monitor the state of AAA from a different perspective. These findings suggest that variations in morphology and hemodynamics within the local segment of the AAA might serve as an alternative approach for predicting the rupture risk of AAA.

Simulated synapse loss induces depression-like behaviors in deep reinforcement learning

Deep Reinforcement Learning is a branch of artificial intelligence that uses artificial neural networks to model reward-based learning as it occurs in biological agents. Here we modify a Deep Reinforcement Learning approach by imposing a suppressive effect on the connections between neurons in the artificial network—simulating the effect of dendritic spine loss as observed in major depressive disorder (MDD). Surprisingly, this simulated spine loss is sufficient to induce a variety of MDD-like behaviors in the artificially intelligent agent, including anhedonia, increased temporal discounting, avoidance, and an altered exploration/exploitation balance. Furthermore, simulating alternative and longstanding reward-processing-centric conceptions of MDD (dysfunction of the dopamine system, altered reward discounting, context-dependent learning rates, increased exploration) does not produce the same range of MDD-like behaviors. These results support a conceptual model of MDD as a reduction of brain connectivity (and thus information-processing capacity) rather than an imbalance in monoamines—though the computational model suggests a possible explanation for the dysfunction of dopamine systems in MDD. Reversing the spine-loss effect in our computational MDD model can lead to rescue of rewarding behavior under some conditions. This supports the search for treatments that increase plasticity and synaptogenesis, and the model suggests some implications for their effective administration.

Biomechanical evaluation of stability after mandibular sagittal split osteotomy for advancement by Obwegeser-Dal Pont and Puricelli techniques using three-dimensional finite elements.

The surgical treatment for mandibular repositioning using a bilateral sagittal split osteotomy (BSSO) favours the development of techniques that result in adequate repair and stability. In Puricelli's mandibular sagittal split osteotomy (PMSSO) proposal, the vertical lateral cut osteotomy is located in the interradicular space between the lower first molar and second premolar. This in silico study aimed to investigate the mechanical stability of PMSSO and compare it with the classical Obwegeser-Dal Pont technique for mandibular advancement. A computational geometric model of the mandible was created in a virtual environment using computer-aided design (CAD) software. After reproducing the advancements, two test groups were developed: GTOD10, Obwegeser-Dal Pont osteotomy, and GTP10, Puricelli osteotomy, both simulating a 10-mm mandibular advancement, allowing for measuring the area of overlap between bone segments. With the geometric changes promoted by the osteotomy, boundary conditions of displacement and force were applied to a CAD software based on finite element analysis (FEA), allowing for quantitative and comparative analysis of the stress and vertical displacement of the mandible, mechanical measurements that may be associated with strength and stiffness. A 17.48% higher stress was observed in the GTP10 group than in GTOD10. However, the region of highest stress in GTP10 was found in a part of the bone that was still intact and far from the area of fragility caused by lateral vertical osteotomy. In contrast, in GTOD10, the region with high stress was in a less resistant bone region. The GTP10 group showed a 28.73% lower displacement than GTOD10. The area of overlap between the proximal and distal segments of the mandible was 33.13% larger in the GTP10 than in the GTOD10 group. The PMSSO method, performed in large mandibular advancements, keeps the point of highest stress away from the mandibular fragility zone. Considering the same amount of advancement, it also promotes less displacement and larger areas of bone overlap. The results suggest that PMSSO, applied in large mandibular advancement, presents greater postoperative stability.

Numerical Analysis and Validation of Horizontal and Vertical Displacements of a Floating Body for Different Wave Periods

This study concentrates on numerically evaluating the behavior of a floating body with a box format. Although research on floating objects has been conducted, the numerical modeling of Wave Energy Converter (WEC) devices, considering the effects of fluctuations, remains underexplored. Therefore, this research intends to facilitate the analysis of floating devices. First, the experimental data served as a benchmark for evaluating the motion paths of the floating box’s centroid. Second, the effects of various wave periods and heights on the floating body’s movement were analyzed. The Volume of Fluid (VOF) multiphase model was applied to simulate the interactions between phases. The computational model involved solving governing equations of mass conservation, volumetric fraction transport, and momentum, employing the Finite Volume Method (FVM). The validation demonstrated that the Normalized Root Mean Square Error (NRMSE) for the x/h ratio was 3.3% for a wave height of 0.04 m and 4.4% for a wave height of 0.1 m. Moreover, the NRMSE for the z-coordinate to the depth of water (z/h) was higher, at 5% for a wave height of 0.04 m and 5.8% for a wave height of 0.1 m. The overall NRMSE remained within acceptable ranges, indicating the reliability of the numerical solutions. Additionally, the analysis of horizontal and vertical velocities at different wave periods and heights showed that for H = 0.04 m, the wave periods had a minimal impact on the amplitude, but the oscillation frequency varied. At H = 0.1 m, both velocities exhibited significantly larger amplitudes, especially for T = 1.2 s and T = 2.0 s, indicating stronger motion with higher wave heights.

Enhanced surface emission of bismuth-ion doped glass by adding metal nanoparticles

Enhancing the luminescent properties of doped silica glass has garnered significant interest due to its potential applications in photonics and optoelectronics. In this study, we enhance the surface emission of bismuth(Bi)-doped silica glass by incorporating metal nanoparticles. Utilizing finite-difference time-domain (FDTD) simulations, we observed a significant increase in surface emission following population inversion. We developed a novel algorithm to achieve a uniform distribution of nanoparticles in a two-dimensional computational model, ensuring that the distribution is physically accurate. Through systematic investigation, we explored the effects of the number, distribution, and size of silver nanoparticles on the surface emission enhancement of Bi-doped glass. Our results demonstrate that under optimal conditions, the surface emission enhancement can reach nearly 72%. Additionally, we evaluated the impact of various metal nanoparticles, finding that gold, silver, copper, and platinum positively influence surface emission enhancement, while titanium has an inhibitory effect. This study underscores the potential of metal nanoparticles to significantly improve the luminescent properties of doped glass, paving the way for advanced applications in photonic devices.

SEC-SAXS/MC Ensemble Structural Studies of the Microtubule Binding Protein Cdt1 Show Monomeric, Folded-Over Conformations.

Cdt1 is a mixed folded protein critical for DNA replication licensing and it also has a "moonlighting" role at the kinetochore via direct binding to microtubules and the Ndc80 complex. However, it is unknown how the structure and conformations of Cdt1 could allow it to participate in these multiple, unique sets of protein complexes. While robust methods exist to study entirely folded or unfolded proteins, structure-function studies of combined, mixed folded/disordered proteins remain challenging. In this work, we employ orthogonal biophysical and computational techniques to provide structural characterization of mitosis-competent human Cdt1. Thermal stability analyses shows that both folded winged helix domains1 are unstable. CD and NMR show that the N-terminal and linker regions are intrinsically disordered. DLS shows that Cdt1 is monomeric and polydisperse, while SEC-MALS confirms that it is monomeric at high concentrations, but without any apparent inter-molecular self-association. SEC-SAXS enabled computational modeling of the protein structures. Using the program SASSIE, we performed rigid body Monte Carlo simulations to generate a conformational ensemble of structures. We observe that neither fully extended nor extremely compact Cdt1 conformations are consistent with SAXS. The best-fit models have the N-terminal and linker disordered regions extended into the solution and the two folded domains close to each other in apparent "folded over" conformations. We hypothesize the best-fit Cdt1 conformations could be consistent with a function as a scaffold protein that may be sterically blocked without binding partners. Our study also provides a template for combining experimental and computational techniques to study mixed-folded proteins.

How deep is your art: An experimental study on the limits of artistic understanding in a single-task, single-modality neural network.

Computational modeling of artwork meaning is complex and difficult. This is because art interpretation is multidimensional and highly subjective. This paper experimentally investigated the degree to which a state-of-the-art Deep Convolutional Neural Network (DCNN), a popular Machine Learning approach, can correctly distinguish modern conceptual art work into the galleries devised by art curators. Two hypotheses were proposed to state that the DCNN model uses Exhibited Properties for classification, like shape and color, but not Non-Exhibited Properties, such as historical context and artist intention. The two hypotheses were experimentally validated using a methodology designed for this purpose. VGG-11 DCNN pre-trained on ImageNet dataset and discriminatively fine-tuned was trained on handcrafted datasets designed from real-world conceptual photography galleries. Experimental results supported the two hypotheses showing that the DCNN model ignores Non-Exhibited Properties and uses only Exhibited Properties for artwork classification. This work points to current DCNN limitations, which should be addressed by future DNN models.

Experimental Verification of a Compressor Drive Simulation Model to Minimize Dangerous Vibrations

The article highlights the importance of analytical computational models of torsionally oscillating systems and their simulation for estimating the lowest resonance frequencies. It also identifies the pitfalls of the application of these models in terms of the accuracy of their outputs. The aim of the paper is to control the dangerous vibration of a mechanical system actuator using a pneumatic elastic coupling using different approaches such as analytical calculations, experimental measurement results, and simulation models. Based on the known mechanical properties of the laboratory system, its dynamic model in the form of a twelve-mass chain torsionally oscillating mechanical system is developed. Subsequently, the model is reduced to a two-mass system using the method of partial frequencies according to Rivin. The total load torque of the piston compressor under fault-free and fault conditions is simulated to obtain the amplitudes and phases of the harmonic components of the dynamic torque. After calculating the natural frequency and the natural shape of the oscillation, the Campbell diagram is processed to determine the critical revolutions. There is a pneumatic flexible coupling between the rotating masses, which changes the dynamic torsional stiffness. The dynamic torque curves transmitted by the coupling are compared with different dynamic torsional stiffnesses during steady-state operation and one cylinder failure. The monitored values are the position of the critical revolutions, the natural frequency, the natural shape of the oscillation, and the RMS of the dynamic load torque. The experimental model is verified by the simulation model. The accuracy of the developed simulation model with the experimental data are apparently very good (even more than 99% of the critical revolutions value obtained by calculation); however, it depends on the dynamic stiffness of the coupling. In this study, a detailed, comprehensive approach combining analytical procedures with simulation models is presented. Experimental data are verified with simulation results, which show a good agreement in the case of 700 kPa coupling pressure. The inaccuracy of some of the experiments (at 300 and 500 kPa pressures) is due to the interaction of the coupling’s apparent stiffness and the level of the damped vibration energy in the coupling, which is manifested by its different heating. Based on further experiments, a solution to these problems will be proposed by introducing this phenomenon effectively into the simulation model.

During haptic communication, the central nervous system compensates distinctly for delay and noise.

Physically connected humans have been shown to exploit the exchange of haptic forces and tactile information to improve their performance in joint action tasks. As human interactions are increasingly mediated through robots and networks it is important to understand the impact that network features such as lag and noise may have on human behaviour. In this paper, we investigated interaction with a human-like robot controller that provides similar haptic communication behaviour as human-human interaction and examined the influence and compensation mechanisms for delay and noise on haptic communication. The results of our experiments show that participants can perceive a difference between noise and delay, and make use of compensation mechanisms to preserve performance in both cases. However, while noise is compensated for by increasing co-contraction, delay compensation could not be explained by this strategy. Instead, computational modelling suggested that a distinct mechanism is used to compensate for the delay and yield an efficient haptic communication.

The Algorithmic Agent Perspective and Computational Neuropsychiatry: From Etiology to Advanced Therapy in Major Depressive Disorder

Major Depressive Disorder (MDD) is a complex, heterogeneous condition affecting millions worldwide. Computational neuropsychiatry offers potential breakthroughs through the mechanistic modeling of this disorder. Using the Kolmogorov theory (KT) of consciousness, we developed a foundational model where algorithmic agents interact with the world to maximize an Objective Function evaluating affective valence. Depression, defined in this context by a state of persistently low valence, may arise from various factors—including inaccurate world models (cognitive biases), a dysfunctional Objective Function (anhedonia, anxiety), deficient planning (executive deficits), or unfavorable environments. Integrating algorithmic, dynamical systems, and neurobiological concepts, we map the agent model to brain circuits and functional networks, framing potential etiological routes and linking with depression biotypes. Finally, we explore how brain stimulation, psychotherapy, and plasticity-enhancing compounds such as psychedelics can synergistically repair neural circuits and optimize therapies using personalized computational models.

Coordinated inheritance of extrachromosomal DNAs in cancer cells.

The chromosomal theory of inheritance dictates that genes on the same chromosome segregate together while genes on different chromosomes assort independently1. Extrachromosomal DNAs (ecDNAs) are common in cancer and drive oncogene amplification, dysregulated gene expression and intratumoural heterogeneity through random segregation during cell division2,3. Distinct ecDNA sequences, termed ecDNA species, can co-exist to facilitate intermolecular cooperation in cancer cells4. How multiple ecDNA species within a tumour cell are assorted and maintained across somatic cell generations is unclear. Here we show that cooperative ecDNA species are coordinately inherited through mitotic co-segregation. Imaging and single-cell analyses show that multiple ecDNAs encoding distinct oncogenes co-occur and are correlated in copy number in human cancer cells. ecDNA species are coordinately segregated asymmetrically during mitosis, resulting in daughter cells with simultaneous copy-number gains in multiple ecDNA species before any selection. Intermolecular proximity and active transcription at the start of mitosis facilitate the coordinated segregation of ecDNA species, and transcription inhibition reduces co-segregation. Computational modelling reveals the quantitative principles of ecDNA co-segregation and co-selection, predicting their observed distributions in cancer cells. Coordinated inheritance of ecDNAs enables co-amplification of specialized ecDNAs containing only enhancer elements and guides therapeutic strategies to jointly deplete cooperating ecDNA oncogenes. Coordinated inheritance of ecDNAs confers stability to oncogene cooperation and novel gene regulatory circuits, allowing winning combinations of epigenetic states to be transmitted across cell generations.

Design and construction of a custom implant of the temporomandibular joint produced by additive manufacturing in titanium alloy from computed tomography

Biomodels are physical reproductions of anatomical structures of regions or organs of the human body used for diagnosis and surgical planning. The use of tomographic images for generating 3D models and manufacturing of biomodels has been awakening a great interest in the medical area. It is possible, with the use of medical images, to generate representative computer models, thus enabling several simulations and biomechanical analysis of the region or organ of interest, aiming at the manufacture of customized prosthesis or orthesis. In this work we present the project of a customized implant of the TMJ, mechanically ordered and manufactured in titanium alloy by the additive manufacturing process of DMLS. Through the model created for the TMJ region, computer simulations of stresses and deformations were performed on the mandible virtually implanted in the patient, considering severe stresses of human chewing applied to the frontal teeth. With the data from the computer simulations the prosthesis was analyzed through a conventional analytical design process to verify the fatigue failure resistance. The implant was fabricated in titanium alloy by additive manufacturing and was realized physical simulations of the coupling of the prosthesis in the biomodel of the mandible fabricated by three-dimensional printing.

Accuracy of Three-Dimensional Neo Left Ventricular Outflow Tract Simulations With Transcatheter Mitral Valve Replacement in Different Mitral Phenotypes.

Transcatheter mitral valve replacement (TMVR) is emerging in the context of annular calcification (valve-in-MAC; ViMAC), failing surgical mitral annuloplasty (mitral-valve-in-ring; MViR) and failing mitral bioprosthesis (mitral-valve-in-valve; MViV). A notorious risk of TMVR is neo left ventricular outflow tract (neo-LVOT) obstruction. Three-dimensional computational models (3DCM) are derived from multi-slice computed tomography (MSCT) and aim to predict neo-LVOT area after TMVR. Little is known about the accuracy of these neo-LVOT predictions for various mitral phenotypes. Preprocedural 3DCMs were created for ViMAC, MViR and MViV cases. Throughout the cardiac cycle, neo-LVOT dimensions were semi-automatically calculated on the 3DCMs. We compared the predicted neo-LVOT area on the preprocedural 3DCM with the actual neo-LVOT as measured on the post-procedural MSCT. Across 12 TMVR cases and examining 20%-70% of the cardiac phase, the mean difference between predicted and post-TMVR neo-LVOT area was -23 ± 28 mm2 for MViR, -21 ± 34 mm2 for MViV and -73 ± 61 mm2 for ViMAC. The mean intra-class correlation coefficient for absolute agreement between predicted and post-procedural neo-LVOT area (throughout the whole cardiac cycle) was 0.89 (95% CI 0.82-0.94, p < 0.001) for MViR, 0.81 (95% CI 0.62-0.89, p < 0.001) for MViV, and 0.41 (95% CI 0.12-0.58, p = 0.002) for ViMAC. Three-dimensional computational models accurately predict neo-LVOT dimensions post TMVR in MViR and MViV but not in ViMAC. Further research should incorporate device host interactions and the effect of changing hemodynamics in these simulations to enhance accuracy in all mitral phenotypes.

Modeling Fibrin Accumulation on Flow-Diverting Devices for Intracranial Aneurysms.

The mechanisms leading to aneurysm occlusion after treatment with flow-diverting devices are not fully understood. Flow modification induces thrombus formation within the aneurysm cavity, but fibrin can simultaneously accumulate and cover the device scaffold, leading to further flow modification. However, the interplay and relative importance of these processes are not clearly understood. A computational model of fibrin accumulation and flow modification after flow diversion treatment of cerebral aneurysms has been developed under the guidance of invitro experiments and observations. The model is based on the loose coupling of flow and transport-reaction equations that are solved separately by independent codes. Interaction or reactive terms account for thrombin production from prothrombin stimulated by thrombogenic metallic wires and inhibition by antithrombin as well as fibrin production from fibrinogen stimulated by thrombin and flow shear stress, and fibrin adhesion to device wires and already attached fibrin. The computational model was demonstrated and tested on idealized vessel and aneurysm geometries. The model was able to reproduce the salient features of fibrin accumulation after the deployment of flow-diverting devices in idealized invitro models of cerebral aneurysms. Namely, fibrin production in regions of high shear stress, initial accumulation at the inflow zone, and progressive occlusion of the device and corresponding flow attenuation. The computational model linking flow dynamics to fibrin production, transport, and adhesion can be used to investigate and better understand the effects that lead to fibrin accumulation and the resulting aneurysm inflow reduction and intra-aneurysmal flow modulation.

The HVAC system engineering method for a new family of unified cabins of combine harvesters

BACKGROUND: Designing a combine harvester cabin climate system is a complex and multi-stage process that includes solving a set of tasks. To solve them, a design engineer must have knowledge and skills in various fields, starting with thermal engineering calculations and ending with experimental research methods and computer modeling, which requires a large amount of intellectual and time resources. Therefore, the task of creating a unified method of engineering of combine harvester cabin HVAC system is highly relevant. AIM: Development of the HVAC system for a unified cabin of a grain harvester and a forage harvester. The designed HVAC system is purposed to create a comfortable microclimate in the cabin of the combine for 2 people in summer and winter operating conditions. METHODS: The methodology of calculation and selection of the combine harvester cabin HVAC system, which includes the development of engineering calculation methods and mathematical modeling of thermodynamic and ventilation processes in the cabin, is considered. RESULTS: The main parameters were determined: heat intakes and heat losses for the grain harvester and forage harvester cabins, which amounted to 2.8 and 2.2 kW for the grain harvester, 2.9 and 2.35 kW for the forage harvester; the required air flow rate supplied to the cabin to ensure a comfortable temperature — 740 m3/h; the percentage of air recirculation regarding the conditions of absence of fogging and creation of excessive pressure in the cabin — 75%; the cooling and heating capacity of the HVAC system, taking into account the operating conditions of the combine, are 7.8 and 6.3 kW respectively. The selection of the main equipment of the HVAC system for a unified cabin for a new family of unified cabins of combines — the BUHLER 1000 MFWD evaporator-heater and the Valeo TM16 compressor are chosen. CONCLUSION: The HVAC system designed in accordance with the presented methodology is capable of ensuring a comfortable cabin air temperature in the range of 22–24 °C at various operating modes of the combine harvester in different regions. In addition, the HVAC system parameters eliminate fogging of the cabin windows and the penetration of dusty air inside due to the created excessive pressure.