Simulated Model Research Articles

Fiscal Stance, Fiscal Rules, and Public Debt Dynamics in the United States

ABSTRACTPublic debt in the United States has continued to rise despite the implementation of various policy measures aimed at curbing its growth. The Autoregressive Distributed Lag (ARDL) and Dynamic Simulated ARDL models were used to examine the effect of fiscal stance and fiscal rules on the dynamics of US public debt. The findings reveal that while fiscal tightening is useful in reducing public debt accumulation, the adoption of fiscal rules may be unbeneficial for curtailing public debt, implying that discretion, rather than rules, may be more desirable for public debt management in the United States.

High-efficiency Lead-free Perovskite Solar Cells with MXene and NiO as Transport Layers: A Numerical Study

Abstract Perovskite solar cells (PSC) are attracting a lot of interest in the scientific communities. It is one of the most promising candidates for solar cells, providing a maximum efficiency of about 30% in recent years. These PSCs could be fine-tuned to enhance their performance using various transport layers. This work demonstrates the design and analyses of the lead-free, MXene-based solar cell model with the flexible architecture of FTO/ Mo2TiC2/CH3NH3SnI3/NiO. The attention has been focused on the novel two-dimensional materials called MXenes (Mo2TiC2 in present study) as electron transport layer (ETL) and NiO as hole transport layer (HTL). We have demonstrated the effect of transport layers on the overall performance of the device by varying parameters such as thickness, electron affinity, defect density of the absorber, and doping concentrations. The simulations were conducted using the Solar Cell Capacitance Simulator (SCAPS-1D) software to evaluate the performance of the solar cell. Comparisons with different HTL and back metal contacts are also studied for better understanding of the performance. The simulated model shows that the presented device structure has a potential to achieve a maximum efficiency of about 31%. This simulation is expected to provide valuable insight to the solar cell research community to explore MXene based solar cells to investigate further to maximize the efficiency and achieve the practical device.

Calibrar: An R package for fitting complex ecological models

Abstract The fitting or parameter estimation of complex ecological models is a challenging optimisation task, with a notable lack of tools for fitting complex, long runtime or stochastic models. calibrar is an R package that is dedicated to the fitting of complex models to data. It is a generic tool that can be used for any type of model, especially those with non‐differentiable objective functions and long runtime, including individual or agent based models. calibrar supports multiple phases and constrained optimisation, includes 20 optimisation algorithms, including derivative‐based and heuristic ones. It supports any type of parallelisation, the capability to restart interrupted optimisations for long runtime models and the combination of different optimisation methods during the multiple phases of a calibration. User‐level expertise in R is necessary to handle calibration experiments with calibrar, but there is no need to modify the model's code, which can be programmed in any language. It implements maximum likelihood estimation methods and automated construction of the objective function from simulated model outputs. For more experienced users, calibrar allows the implementation of user‐defined objective functions. The package source code is fully accessible and can be installed directly from CRAN.

A joint three-plane physics-constrained deep learning based polynomial fitting approach for MR electrical properties tomography.

Magnetic resonance electrical properties tomography can extract the electrical properties of in-vivo tissue. To estimate tissue electrical properties, various reconstruction algorithms have been proposed. However, physics-based reconstructions are prone to various artifacts such as noise amplification and boundary artifact. Deep learning-based approaches are robust to these artifacts but need extensive training datasets and suffer from generalization to unseen data. To address these issues, we introduce a joint three-plane physics-constrained deep learning framework for polynomial fitting MR-EPT by merging physics-based weighted polynomial fitting with deep learning. Within this framework, deep learning is used to discern the optimal polynomial fitting weights for a physics based polynomial fitting reconstruction on the complex B1+ data. For the prediction of optimal fitting coefficients, three neural networks were separately trained on simulated heterogeneous brain models to predict optimal polynomial weighting parameters in three orthogonal planes. Then, the network weights were jointly optimized to estimate the polynomial weights in each plane for a combined conductivity reconstruction. Based on this physics-constrained deep learning approach, we achieved an improvement of conductivity estimation accuracy in comparison to a single plane estimation and a reduction of computational load. The results demonstrate that the proposed method based on 3D data exhibits superior performance in comparison to conventional polynomial fitting methods in terms of capturing anatomical detail and homogeneity. Crucially, in-vivo application of the proposed method showed that the method generalizes well to in-vivo data, without introducing significant errors or artifacts. This generalization makes the presented method a promising candidate for use in clinical applications.

Static and dynamic in vitro colonic models reveal the spatiotemporal production of flavan-3-ol catabolites.

Flavan-3-ols are the most found flavonoid compounds in the human diet. Polymeric and monomeric flavan-3-ols reach the colonic region intact, where the gut microbiota utilizes them as substrates. In this research work, we investigated the pattern of colonic metabolites associated with flavan-3-ols, conducting a comprehensive analysis that combined (un)targeted metabolomics and in vitro colonic models. Firstly, the proposed flavan-3-ol metabolic pathway was investigated in-depth using a static in vitro model inoculated with different fecal donors. An apple, (-)-epicatechin, and procyanidin C1 were employed as feeding conditions. Small phenolic acids, such as phenylpropanoic acid and 3,4-dihydroxybenzoic acid, were positively associated with the apple feeding condition. In contrast, 5-(3',4'-dihydroxyphenyl)-γ-valerolactone and other specific early intermediates like phenylvaleric acids were positively associated with (-)-epicatechin. Secondly, by employing a dynamic in vitro simulator model of the human digestion system (SHIME), we reconstructed the flavan-3-ol metabolic pathway regionally. In the proximal colon region, we localized catabolites, such as 5-(3',4'-dihydroxyphenyl)-γ-valerolactone, while in the distal region, we identified mainly small phenolics. Combining static and dynamic in vitro models, we observed differences in the release of flavan-3-ol catabolites, influenced by both the food structure (isolated compounds and a food matrix) and the colonic region. This study sheds light on the colonic catabolism of one of the main dietary (poly)phenols and localizes microbial metabolites.

On the Response of Massive Main Sequence Stars to Mass Accretion and Outflow at High Rates

Abstract With a one-dimensional stellar evolution model, we find that massive main sequence stars can accrete mass at very high mass accretion rates without expanding much if they lose a significant fraction of this mass from their outer layers simultaneously with mass accretion. We assume the accretion process is via an accretion disk that launches powerful jets from its inner zones. These jets remove the outer high-entropy layers of the mass-accreting star. This process operates in a negative feedback cycle, as the jets remove more envelope mass when the star expands. With the one-dimensional model, we mimic the mass removal by jets by alternating mass addition and mass removal phases. For the simulated models of 30M ⊙ and 60M ⊙, the star does not expand much if we remove more than about half of the added mass in not-too-short episodes. This holds even if we deposit the energy the jets do not carry into the envelope. As the star does not expand much, its gravitational potential well stays deep, and the jets are energetic. These results are relevant to bright transient events of binary systems powered by accretion and the launching of jets, e.g., intermediate luminosity optical transients, including some luminous red novae, the grazing envelope evolution, and the 1837–1856 Great Eruption of Eta Carinae.

Impact of Road Gradient on Fuel Consumption of Light-Duty Diesel Vehicles

The geometric alignment of highways directly affects the fuel consumption of motor vehicles. To analyze the impact of the road gradient on fuel consumption, actual road tests were conducted in plateau and mountainous areas. Geographic Information Systems (GISs) were used to calculate road gradients, and Vehicle Specific Power (VSP) distributions were obtained through testing, serving as inputs for the motor vehicle emission simulator (MOVES) model. Finally, the simulation results were verified against the experimental results. The findings indicate a strong positive correlation between road gradients ranging from −5% to +5%, the VSP, and fuel consumption. At a constant gradient, the fuel consumption rate increases with the vehicle speed; the fuel consumption factor is lowest at 60 km/h and highest at 40 km/h. Under both constant and actual driving speeds, when the absolute values of uphill and downhill gradients are the same, the average fuel consumption for both uphill and downhill driving shows that, at gradients of 1% to 3%, the fuel savings from downhill driving can offset the additional fuel consumption on uphill driving. At gradients of 4% to 6%, the increase in fuel consumption on uphill driving surpasses the savings from downhill driving. During uphill climbs, lower speeds within the mid-to-low range result in lower fuel consumption and greater reserve power. The MOVES model demonstrates good adaptability in plateau and mountainous areas.

Concurrent Micra Leadless Pacemaker Implantation and AVN Ablation: Computer Modeling of Novel Risk Mitigation Strategy.

Concurrent Micra leadless pacemaker (Medtronic, Minneapolis, Minnesota) implantation and atrioventricular node (AVN) ablation has been shown to be feasible and safe in patients with symptomatic, drug-refractory atrial fibrillation (AF). However, major complications within the 30 days after concurrent Micra implantation and AVN ablation have been reported. We evaluated the efficacy and safety of the concurrent procedure at our institution. We conducted a single-center, retrospective case series of patients who underwent concurrent Micra implantation and radiofrequency (RF) AVN ablation from January 2019 to May 2023. A simulated computer model was created to characterize the interaction between the dissipated power at the Micra cathodal electrode as a function of the distance between the RF ablation catheter and the location of the return electrode. Fifteen patients were included. Most were elderly, White, female, and had persistent AF. One had transient, acute loss of ventricular capture that resulted in asystole and required emergent pacing from the ablation catheter. A proposed strategy of moving the RF return electrode to a cranial position from a caudal position was shown by computer modeling to direct more RF current away from the Micra and lower the dissipated power at the Micra cathodal electrode. Concurrent Micra implantation and AVN ablation is feasible and safe and has high procedural success. An acute rise in pacing threshold can occur from RF energy, resulting in asystole. Computer modeling showed that placing the RF return electrode in the cranial position resulted in lower dissipated power at the Micra cathodal electrode.

Mechanical Properties and Prediction Based on ANN Model of Basalt and Polypropylene Fiber Reinforced Concrete

Objectives: Utilization of steel fiber increases concrete strength and ductility however, the cost of steel fiber is high and ultimately leads to an increase in the cost of concrete. Basalt fiber is a new type of fiber with high strength and toughness and can effectively replace the place of steel fibers. In this paper, concrete reinforced with polypropylene fibers and basalt fibers was tested for its mechanical properties and compared with the same predicted through a simulated model created by the Artificial Neural Network. Methods: The percentage of polypropylene fiber was kept at 0.25%, and different amounts of basalt fiber were added as 0%, 0.25%, 0.5%, 0.75%, and 1%. Based on traditional testing methods compressive strength, tensile strength, and flexural strength of normal concrete, Fiber reinforced concrete and Hybrid fiber reinforced concrete have been found. To forecast the concrete qualities, the model was simulated after the datasets were trained using a neural network fitting tool. Findings: In comparison to concrete that had only 0.25% polypropylene fiber, a hybrid fiber-reinforced concrete mix including 0.5% basalt fiber and 0.25% polypropylene fiber demonstrated a 9.69% improvement in compressive strength. In comparison to the concrete made only of polypropylene, the mix containing 0.25% polypropylene fiber and 0.75% basalt fiber showed a 26.12% increase in flexural strength and a 39.25% increase in split tensile strength. The error percentage between predicted and experimental values is below 10%. Novelty: Utilization of basalt fiber in concrete is very rare and this proposed work focused on the hybridization of basalt fiber in the place of steel fibers, which is prone to corrosion and reduces the cost of material effectively. The concrete's flexural and tensile strengths were significantly increased by the addition of basalt fibers. The mechanical characteristics of hybrid fiber-reinforced concrete are well predicted by the simulated ANN model. Keywords: Basalt fiber, Polypropylene fiber, Artificial Neural network, Fiber reinforced concrete, Hybridization

Two algorithms for improving model-based diagnosis using multiple observations and deep learning.

Model-based diagnosis (MBD) is a critical problem in artificial intelligence. Recent advancements have made it possible to address this challenge using methods like deep learning. However, current approaches that use deep learning for MBD often struggle with accuracy and computation time due to the limited diagnostic information provided by a single observation. To address this challenge, we introduce two novel algorithms, Discret2DiMO (Discret2Di with Multiple Observations) and Discret2DiMO-DC (Discret2Di with Multiple Observations and Dictionary Cache), which enhance MBD by integrating multiple observations with deep learning techniques. Experimental evaluations on a simulated three-tank model demonstrate that Discret2DiMO significantly improves diagnostic accuracy, achieving up to a 685.06% increase and an average improvement of 59.18% over Discret2Di across all test cases. To address computational overhead, Discret2DiMO-DC additionally implements a caching mechanism that eliminates redundant computations during diagnosis. Remarkably, Discret2DiMO-DC achieves comparable accuracy while reducing computation time by an average of 95.74% compared to Discret2DiMO and 89.42% compared to Discret2Di, with computation times reduced by two orders of magnitude. These results indicate that our proposed algorithms significantly enhance diagnostic accuracy and efficiency in MBD compared with the state-of-the-art algorithm, highlighting the potential of integrating multiple observations with deep learning for more accurate and efficient diagnostics in complex systems.

Surgical skills education and training in fetoscopy and fetal surgery

Abstract Introduction Surgical education must constantly evolve to align with the most current methods of surgical practice. This paper presents our experience organizing ten international editions of a training course in fetoscopic surgery. We have developed various inanimate training models and a sheep-animal model for fetal surgery training and research, which are both practical and reproducible. Methods Various practices and surgical sessions are developed during training: Knowledge of sheep anatomy and physiology as a model of pregnancy, introduction to basic fetoscopy equipment, exploration of the uterine cavity, use of the Nd-YAG laser for photocoagulation of placental vessels, resolution of fetal and amniotic constriction bands, and endoscopic balloon tracheal occlusion (for the treatment of severe CDH). Results Fetoscopic surgery is technically challenging, and learning new skills in a clinical setting can be risky. Successful performance requires acquiring a new set of technical skills and familiarity with specific surgical procedures. Understanding how to use the fetoscope, learning intrauterine vessel orientation, and becoming familiar with laser coagulation before operating on patients can reduce surgical complications. This training can shorten the learning curve for new surgeons and reinforce practice among experts as fundamental skills are developed using realistic tissue models and surgical simulators. Conclusion The sequential skill acquisition programme using a simulator and animal model is transferable to human patients and shortens the learning curve for new specialists, although expert consultation is recommended in the initial phase to minimize potential complications.

Predicting indoor temperature and humidity in a naturally ventilated office room using long short-term memory networks model in a tropical climate

ABSTRACT This study evaluates the efficacy of long short-term memory (LSTM) neural networks in predicting indoor temperature and humidity dynamics for naturally ventilated office environments in Ho Chi Minh City, Vietnam (10.8231°N, 106.6297°E). The tropical climate of this location, characterized by consistently warm temperatures year-round, provides a specific context for the model's performance and applicability. A simulated office room model with diverse window opening scenarios was developed using EnergyPlus simulations, generating a synthetic dataset of hourly indoor-outdoor conditions across varying seasons. An LSTM neural network, trained on 70% of the data and tested on the remaining 30%, was employed to forecast indoor temperature and humidity at multiple time horizons. Results indicate that the LSTM achieves near-perfect short-term (1–30 min) predictions, with performance degrading at longer horizons (60–120 min) while remaining competitive with existing approaches. The model effectively captured the strong influence of window opening area and solar irradiance on indoor conditions. A comparison of different window configurations revealed their significant impact on predicted thermal dynamics. Model accuracy was assessed using the coefficient of determination, root mean square error, and mean absolute error metrics. The study demonstrates that LSTM networks can effectively learn complex non-linear building physics to forecast climate in naturally ventilated spaces. With sub-second response times, this approach shows potential for supporting real-time control and optimization of natural ventilation strategies based on probabilistic forecasts, ultimately enhancing occupant comfort and energy efficiency in buildings.

Large Eddy Simulations of a Medium-Scale Ethanol Pool Fire Using Conditional Source-Term Estimation Including Coupled Soot Radiation Effects

ABSTRACT The objective of this paper is to assess the combined capabilities of Conditional Source-Term Estimation with radiation and soot modeling in large eddy simulations of a medium-scale ethanol pool fire. Tabulated detailed chemistry is implemented including radiative losses. The radiative transfer equation is solved with the weighted sum of gray gas models to determine the gaseous absorption. Two subgrid soot models are considered using the laminar smoke point concept. The first approach calculates the soot formation and oxidation rates corrected to account for turbulent effects. The second determines the soot reaction rates in conditional space using analytical functions and the filtered reaction rates are determined by convolution with the filtered mixture fraction density function. Predictions of the time-averaged and root mean square temperature, species mole fractions and soot mass fraction are compared with experimental measurements. Numerical temperatures agree well with experiments except in the fire plume where some overpredictions are observed. Species concentrations match the measurements with some discrepancies observed in the products farther downstream, partly explained by the experimental uncertainty. The peak soot predicted with the first model is in reasonable agreement with the experiments but is much lower using the second approach. These differences are explained by the differences in the turbulence soot chemistry treatment and calibration of empirical constants. However, here, soot has a limited impact on the temperature, flow and mixing fields due to low soot concentrations produced by ethanol. The radiative heat fluxes are reasonably well predicted. Further validation is needed with additional experimental soot data.

Examination of the Bioavailability and Bioconversion of Wheat Bran-Bound Ferulic Acid: Insights into Gastrointestinal Processing and Colonic Metabolites.

The postingestion journey and bioconversion of wheat bran-bound ferulic acid, a known beneficial phytochemical, remain insufficiently understood. This study aims to systematically investigate its bioaccessibility, bioavailability, excretion, and colonic metabolism, both in vitro and in vivo. Initial analysis confirmed the abundance and bioactivity of ferulic acid in wheat bran. Using a simulated gastrointestinal model, 1.7% of the ferulic acid was found to be bioaccessible, in contrast to 43.4% for total phenolics. In vivo bioavailability was assessed in rats via oral gavage of cooked wheat bran, with a plasma ferulic acid level peaking at 32.5 ± 4.9 ng/mL, corresponding to an absorption rate of 0.3%, while 1% was excreted in urine. Fecal metabolomic analysis revealed extensive colonic bioconversion, with elevated levels of ferulic acid and its metabolites, including 3-phenylpropionic acid, dihydroferulic acid, 5-hydroxyferulic acid, and hippuric acid. Novel metabolites, such as 3-(2,5-dimethoxyphenyl)propionic acid and N-(2-furoyl)glycine, were detected for the first time. These findings shed light on the complex biotransformation of wheat bran-bound ferulic acid and its potential health implications.

A New Pabs Model for Quantitatively Diagnosing Phosphorus Nutritional Status in Corn Plants

Accurate diagnosis of plant phosphorus nutritional status is critical for optimizing agricultural practices and enhancing resource efficiency. Existing methods are limited to qualitatively assessing plant phosphorus nutritional status and cannot quantitatively estimate the plant’s phosphorus requirements. Moreover, these methods are time-consuming, making them impractical for large-scale application. In this study, we developed an advanced phosphorus absorption model (Pabs) that integrates the phosphorus nutrition index (PNI) and phosphorus use efficiency (PUE). The PUE, a critical metric for assessing phosphate fertilizer use efficiency, was quantified by comparing yields under fertilized and unfertilized conditions. Utilizing the Agricultural Production Systems Simulator (APSIM) model, we simulated maize (Zea mays L.) phosphorus concentration (P) and aboveground biomass (Bio) under varying phosphorus application rates. The model exhibited robust performance, achieving an R2 above 0.95 and an RMSE below 0.22. Based on the APSIM model simulations, a phosphorus dilution curve (Pc = 3.17 Bio−0.29, R2 = 0.98) was established, reflecting the dilution trends of phosphorus across growth stages. Furthermore, the use of vegetation indices (VIS) to evaluate phosphorus nutritional status also showed promising results, with inversion accuracies exceeding 0.70. To validate the model, field sampling was conducted in maize-growing regions of Changchun. Results demonstrated a correct diagnosis rate of 75%, underscoring the model’s capacity to accurately estimate phosphorus requirements on a regional scale. These findings highlight the Pabs model as a reliable tool for precision phosphorus management, offering significant potential to optimize fertilization strategies and support sustainable agricultural systems.

A genealogy-based approach for revealing ancestry-specific structures in admixed populations.

Elucidating ancestry-specific structures in admixed populations is crucial for comprehending population history and mitigating confounding effects in genome-wide association studies. Existing methods for elucidating the ancestry-specific structures generally rely on frequency-based estimates of genetic relationship matrix (GRM) among admixed individuals after masking segments from ancestry components not being targeted for investigation. However, these approaches disregard linkage information between markers, potentially limiting their resolution in revealing structure within an ancestry component. We introduce ancestry-specific expected GRM (as-eGRM), a novel framework for elucidating the relatedness within ancestry components between admixed individuals. The key design of as-eGRM consists of defining ancestry-specific pairwise relatedness between individuals based on genealogical trees encoded in the Ancestral Recombination Graph (ARG) and local ancestry calls and computing the expectation of the ancestry-specific relatedness across the genome. Comprehensive evaluations using both simulated stepping-stone models of population structure and empirical datasets based on three-way admixed Latino cohorts showed that analysis based on as-eGRM robustly outperforms existing methods in revealing the structure in admixed populations with diverse demographic histories. Taken together, as-eGRM has the promise to better reveal the fine-scale structure within an ancestry component of admixed individuals, which can help improve the robustness and interpretation of findings from association studies of disease or complex traits for these understudied populations.

DynamicVLN: Incorporating Dynamics into Vision-and-Language Navigation Scenarios.

Traditional Vision-and-Language Navigation (VLN) tasks require an agent to navigate static environments using natural language instructions. However, real-world road conditions such as vehicle movements, traffic signal fluctuations, pedestrian activity, and weather variations are dynamic and continually changing. These factors significantly impact an agent's decision-making ability, underscoring the limitations of current VLN models, which do not accurately reflect the complexities of real-world navigation. To bridge this gap, we propose a novel task called Dynamic Vision-and-Language Navigation (DynamicVLN), incorporating various dynamic scenarios to enhance the agent's decision-making abilities and adaptability. By redefining the VLN task, we emphasize that a robust and generalizable agent should not rely solely on predefined instructions but must also demonstrate reasoning skills and adaptability to unforeseen events. Specifically, we have designed ten scenarios that simulate the challenges of dynamic navigation and developed a dedicated dataset of 11,261 instances using the CARLA simulator (ver.0.9.13) and large language model to provide realistic training conditions. Additionally, we introduce a baseline model that integrates advanced perception and decision-making modules, enabling effective navigation and interpretation of the complexities of dynamic road conditions. This model showcases the ability to follow natural language instructions while dynamically adapting to environmental cues. Our approach establishes a benchmark for developing agents capable of functioning in real-world, dynamic environments and extending beyond the limitations of static VLN tasks to more practical and versatile applications.

A System Dynamics Simulating Model of Groundwater Level Changes and Its Impacts on Land Uplift

The global effort to transition from fossil fuels to renewable energy sources results in the closure of surface and underground coal mines, subsequently affecting the environment, society, and the economy. Land uplift is one of the challenges since the coal mine closure in Germany. Using computational modeling and simulation helps understand the long-term land uplift behavior related to variables’ complexity and dynamic interactions of groundwater level changes. This study proposes a system dynamics (SD) model to study the complexity of variables and consider the upstream groundwater impacts on land uplift in abandoned mines in the Ruhr district in Germany. The proposed model utilized causal loop diagrams (CLD) and stock flow diagrams (SFD) to provide a conceptual and simulation framework. The individual and combined effects of increasing rainfall, groundwater level, effective stress, porosity, porewater pressure, total stress, etc., on land uplift are studied. The model was evaluated by analyzing different scenarios based on stochastic and random variables in the Eastern Ruhr region of Germany. The scenario result showed that with a 10% increase in groundwater level, the land uplift increased by about + 6%, and a rise of 200% in total stress caused an 18% increase in effective stress, which decreased the land uplift by about 20%. Additionally, decreasing porosity leads to reduced land uplift. The land uplift trend was very high in the first 15 years after mine closure. Some scenarios are considered to delay and decrease the land uplift in the long-term.

The cognitive critical brain: modulation of criticality in perception-related cortical regions

The constantly evolving world necessitates a brain that can swiftly adapt and respond to rapid changes. The brain, conceptualized as a system performing cognitive functions through collective neural activity, has been shown to maintain a resting state characterized by near-critical neural dynamics, positioning it to effectively respond to external stimuli. However, how near-criticality is dynamically modulated during task performance remains insufficiently understood. In this study, we utilized the prototypical Ising Hamiltonian model to investigate the modulation of near-criticality in neural activity at the cortical subsystem level during perceptual tasks. Specifically, we simulated 2D-Ising models in silico using structural MRI data and empirically estimated the system's state in vivo using functional MRI data. We first replicated previous findings that the resting state is typically near-critical as captured by the Ising model. Importantly, we observed heterogeneous changes in criticality across cortical subsystems during a naturalistic movie-watching task, with visual and auditory regions fine-tuned closer to criticality. A more fine-grained analysis of the ventral temporal cortex during an object recognition task further revealed that only regions selectively responsive to a specific object category were tuned closer to criticality when processing that object category. In conclusion, our study provides empirical evidence from the domain of perception supporting the cognitive critical brain hypothesis that modulating the criticality of subsystems within the brain's hierarchical and modular organization may be a fundamental mechanism for achieving diverse cognitive functions.

A Highly Realistic Hydrogel-Based Simulated Vascular Model for Surgical Hemostasis Training

A Highly Realistic Hydrogel-Based Simulated Vascular Model for Surgical Hemostasis Training