Computational Model Research Articles

Deconstructing emotions in self-control through computational modeling

Positive and negative emotions have a determining role in self-control, a vital aspect of human decision-making, defined as the dilemma between a smaller sooner reward and a larger later reward. Self-control, as an internal conflict between the higher (pre-frontal cortex) and the lower (limbic system) parts of the brain, has already been simulated using the Iterated Prisoner’s Dilemma game with learning in a computational model. However, the concept of emotions, defined as states elicited by positive and negative reinforcers, is absent from the existing self-control model. By increasing and decreasing the values of the reinforcement signals in the Prisoner’s Dilemma payoff matrix in-between the rounds, we simulated the increment or decrement of positive or negative emotions’ intensity and thus the effects of the presence of emotions, rather than the emotions per se. Our results reflect the restorative role of positive emotions on self-control, the necessity of negative emotions for successful self-control and the impairment of self-control due to intense negative emotions. Furthermore, our results reveal the importance of parameters in self-regulation, such as the intensity of emotions and the frequency it changes. In conclusion, we incorporated the effect of emotions in a computational model of self-control, and with our results complying with cognitive science literature, we demonstrated the cognitive adequacy of our model. We anticipate in this way to provide novel approaches for comprehending self-control behaviour, and to contribute to the general attempt of modeling human behaviour.

Acute kidney injury subphenotyping and personalized medicine.

This review discusses novel concepts of acute kidney injury (AKI), including subphenotyping, which may facilitate the development of target treatment strategies for specific subgroups of patients to achieve precision medicine. AKI is a multifaceted syndrome with a major impact on morbidity and mortality. As efforts to identify treatment strategies have largely failed, it is becoming increasingly apparent that there are different subphenotypes that require different treatment strategies. Various ways of subphenotyping AKI have been investigated, including the use of novel renal biomarkers, machine learning and artificial intelligence, some of which have already been implemented in the clinical setting. Thus, novel renal biomarkers have been recommended for inclusion in new definition criteria for AKI and for the use of biomarker bundled strategies for the prevention of AKI. Computational models have been explored and require future research. Subphenotyping of AKI may provide a new understanding of this syndrome and guide targeted treatment strategies in order to improve patient outcomes.

Review of cloud computing models in education and the unmet needs

<p>This article thoroughly examines recently proposed cloud computing (CC) models used within the higher educational institutions (HEI) field, scrutinizing their objectives, structures, and incorporated requirements. Each model's unique architecture and functionality are analyzed to understand their potential educational contributions. Beyond technical considerations, the study explores nuanced requirements essential for successful integration in educational settings. The review exposes diverse aims pursued by the models, such as enhanced scalability, collaborative learning, and resource management, emphasizing their capacity to reshape traditional educational paradigms. However, a notable gap emerges-the absence of cultural and requirement elicitation models within the frameworks. Despite growing cultural diversity and varied educational needs, most models lack components addressing cultural nuances and robust requirement elicitation. In conclusion, the paper identifies a pressing need for a transformative shift in developing CC models for education. The absence of dedicated cultural and requirement elicitation models challenges the holistic effectiveness of these frameworks. Future efforts should prioritize integrating culturally sensitive components and comprehensive requirement elicitation strategies to create adaptive, universally applicable, and inclusive CC educational environments. Addressing these gaps will pave the way for a nuanced and responsive integration of CC technologies in diverse educational settings.</p>

A new perspective on Misbeliefs: A computational model for perceived control

The discovery of various cognitive biases and social illusions indicates that people routinely have misbeliefs. Focusing on the illusion of control (IOC), this article argues that when time and cognitive resources are limited, and information is imperfect, misbeliefs can be generated naturally in a normal belief formation system, and these misbeliefs might help people adapt better to the environment.In this study, we present a computational model—the informativeness-weighting model (IWM)—describing how beliefs are revised by observed evidence. To be precise, IOC is the result of distinct types of evidence being endowed with different weights according to its informativeness in a belief revision process. To evaluate the model, we also designed two behavioral experiments to compare people’s sense of control with that predicted by the model.In both experiments, our model outperformed two alternative models in predicting and explaining the misestimation of people’s perceived control. Thus, we suggest that our model reflects an adaptive strategy for information processing, which helps to explain why misbeliefs, like IOC, are prevalent in human cognition.

Character N-gram Model for Toxicity Prediction

Prediction of molecular toxicity plays an important role in drug discovery. It has a direct relationship with human health and medical destiny. Accurately assessing a molecule’s toxicity can aid in the weeding out of low-quality com pounds early in the drug discovery phase, avoiding depletion later in the drug development process. Computational models have been used to automatically predict the molecular toxicity. In this paper, a machine learning-based model has been proposed. TF/IDF representation scheme has been used for N-gram and integrated with SMILE. Multiple machine learning classifiers have been im plemented such as Decision Tree, KNN, MLP, Random Forests, SGD and SVM. RF outperforms all the other classifiers. A wide range of N-gram models have been implemented and trigram reported the best results.

Study on the PDDO-based meshfree method in numerical simulation of shell ductile fracture considering a non-local GTN model

A meshfree method is developed based on the peridynamic differential operator (PDDO) for ductile damage and fracture problems in metal shell structures. The kinematic equations coupled to classical continuum mechanics (CCM) are derived with the motion variables discretized by the PDDO. The elastoplastic and fracture behavior of the material are described by applying the Gurson-Tvergaard-Needleman (GTN) model with shear modification, and the non-local form of the model improves the computational convergence for different modeling scales. The zero-energy model in numerical computations is effectively controlled by introducing an hourglass force based on the average displacement state. The particles contact algorithm and multi-crack visualization algorithm are developed to simulate the fracture of shell structures under collision loads. By comparing with experiments, it is verified that the proposed PDDO-based meshfree method can accurately predict the ductile fracture of shell structures subjected to in-plane and out-of-plane loads.

Numerical investigation on the effect of ignition timing on a low-temperature hydrogen-fueled Wankel rotary engine with passive pre-chamber ignition

Adopting the low-temperature hydrogen evaporated from the liquid hydrogen is capable of improving volumetric efficiency for the Wankel rotary engine (WRE). Considering the difficulty in ignition and slow flame propagation of low-temperature hydrogen-air mixtures, the passive pre-chamber is used to improve ignition and combustion. A three-dimensional computational fluid dynamics model for a turbulent jet ignition (TJI) WRE fueled by low-temperature hydrogen was established. The effects of low temperature and TJI on the in-cylinder flow field, combustion, emissions and leakage in the TJI-WRE fueled by low-temperature hydrogen were studied under different ignition timings. The results indicated that low-temperature tends to suppress the flame propagation, whereas TJI can accelerate the flame speed and promote flame propagation to the unburned zone in the combustion chamber. Combining low-temperature hydrogen with the passive pre-chamber can achieve high engine thermal efficiency and power while significantly reducing leakage. With the ignition timing set at 18 °CA before the top dead center, the indicated thermal efficiency reached 39.49% and the indicated mean effective pressure peaked at 0.77 MPa. Compared to the original engine, fresh mixture leakage through spark plug cavities and adjacent chambers was reduced by 72.13% and 78.79%, respectively.

Hypoxia-driven hybrid phenotypes in Ewing sarcoma: Insights from computational epithelial-mesenchymal transition modeling

Ewing sarcoma (ES) is an extremely aggressive pediatric tumor primarily propelled by the EWS::FLI1 fusion protein. This fusion protein plays a pivotal role in various biological processes within ES, including hypoxia and epithelial-mesenchymal transition (EMT). Hypoxia has been documented to trigger EMT, a process that can stabilize a hybrid cell state, enhancing metastatic potential and resistance to drugs. However, the precise mechanisms that sustain this hybrid phenotype during hypoxia in ES have remained enigmatic. Our study introduces a logical model for EMT in ES, underscoring the potential significance of the EWS::FLI1/miR-145 circuit in inducing hybrid states during hypoxia. Furthermore, our findings underscore the necessity for downregulating EWS::FLI1 to fully activate EMT under hypoxic conditions. This model aligns well with results derived from existing literature. These insights underscore the crucial role of EWS::FLI1 in inducing the hybrid state in ES during hypoxia.

Analytical, Computer and Laboratory Modelling of the Effect of the Fuel used in the Spark Ignition Engine of the Selected Vehicle on the Operating Parameters and Exhaust Gas Composition

Analytical, Computer and Laboratory Modelling of the Effect of the Fuel used in the Spark Ignition Engine of the Selected Vehicle on the Operating Parameters and Exhaust Gas Composition

Multi-view contrastive clustering for cancer subtyping using fully and weakly paired multi-omics data

The identification of cancer subtypes is crucial for advancing precision medicine, as it facilitates the development of more effective and personalized treatment and prevention strategies. With the development of high-throughput sequencing technologies, researchers now have access to a wealth of multi-omics data from cancer patients, making computational cancer subtyping increasingly feasible. One of the main challenges in integrating multi-omics data is handling missing data, since not all biomolecules are consistently measured across all samples. Current computational models based on multi-omics data for cancer subtyping often struggle with the challenge of weakly paired omics data. To address this challenge, we propose a novel unsupervised cancer subtyping model named Subtype-MVCC. This model leverages graph convolutional networks to extract and represent low-dimensional features from each omics data type, using intra-view and inter-view contrastive learning approaches. By incorporating a weighted average fusion strategy to unify the dimension of each sample, Subtype-MVCC effectively handles weakly paired multi-omics datasets. Comprehensive evaluations on established benchmark datasets demonstrate that Subtype-MVCC outperforms nine leading models in this domain. Additionally, simulations with varying levels of missing data highlight the model's robust performance in handling weakly paired omics data. The clinical relevance and survival outcomes associated with the identified subtypes further validate the interpretability and reliability of the clustering results produced by Subtype-MVCC.

Impact of surface roughness on the formation of necking instabilities in additive manufactured porous metal plates subjected to dynamic plane strain stretching

This paper investigates the influence of surface roughness on multiple necking formation in additive manufactured porous ductile plates subjected to dynamic plane strain stretching. For this purpose, we have developed a computational model in ABAQUS/Explicit (2019) which includes surface texture and discrete voids measured from 3D-printed metallic specimens using optical profilometry and X-ray tomography analysis, respectively. The mechanical behavior of the material is described using an elastic–plastic constitutive model, with yielding defined by the isotropic von Mises criterion, an associated flow rule, and a power-law function for the yield stress evolution which depends on plastic strain, plastic strain rate, and temperature. The finite element calculations have been conducted across a broad range of strain rates, from 5000s−1 to 50000s−1, to explore the interactions among inertia, surface roughness, and porosity in determining the necking pattern that emerges in the plates at large strains. The finite element results show that surface roughness induces perturbations in the deformation field of the specimen, which lead to early necking localization, while the location and number of necks formed are primarily controlled by the porous microstructure and the loading rate. The results for the neck spacing have shown quantitative agreement with the analytical stability analysis predictions and the unit-cell finite element calculations reported by Rodríguez-Martínez et al. (2021). Moreover, integrating discrete voids into simulations that already account for surface roughness results in a minor reduction in necking strain: surface roughness and porosity demonstrate similar quantitative impacts on necking ductility, which is primarily influenced by inertia effects at the highest strain rates studied. To the best of the authors’ knowledge, this paper presents the first calculations that explore dynamic plastic localization in additive manufactured metals, incorporating actual surface roughness and explicit void representation derived from experimental measurements. This work marks progress in the analysis of 3D-printed structures under impact loading, aiming to understand and predict the mechanics influencing their energy absorption capacity at high strain rates.

Breast cancer identification using machine learning and hyperparameter optimization

Breast cancer identification can be analyzed through genomic analysis using gene expression data, one type of which is mRNA. This involves analyzing gene expression patterns of breast tissue samples to distinguish breast cancer from healthy tissue or to differentiate subtypes of different breast cancers. This research developed the right computational model for breast cancer classification using machine learning and hyperparameter optimization algorithms. The primary objective of this research is to utilize various machine learning algorithms to classify breast cancer based on gene expression and enhance the models developed in previous studies. This paper provides an extensive literature review of prior breast cancer classification research and offers new theoretical perspectives. This research used a problem-solving approach with conventional machine learning techniques, most notably the decision tree. It also evaluates other machine learning algorithms for comparison, including k-nearest neighbor, naïve bayes, random forest, extra tree classifier, and support vector machine. The evaluation process used classification reports that provide insight into the precision, recall, F1-score, and accuracy of each machine learning model. The evaluation results show that the performance of the decision tree algorithm model is superior and impressive, achieving 99.73% accuracy and a score of 1 for precision, recall, and F1-score.

Intelligent Computational Model for Energy Efficiency and AI Automation of Network Devices in 5G Communication Environment

Intelligent Computational Model for Energy Efficiency and AI Automation of Network Devices in 5G Communication Environment

Numerical research on hydrodynamic performance of toroidal propeller under the influence of geometric parameters

Recent advancements in high-efficiency, low-noise toroidal propellers have garnered significant interest due to their potential to revolutionize maritime propulsion. This study addresses this gap by leveraging established geometrical modeling methods for toroidal propellers to design a novel variant, and assesses its hydrodynamic performance using the Reynolds-averaged Navier-Stokes (RANS) method coupled with the Shear Stress Transport (SST) k-ω turbulence model. First, the paper introduces the toroidal propeller concept and modeling method before analyzing the impact of grid size on numerical simulations. The correspondence between numerical results and experimental data is then established to validate the computational model's accuracy. Following this, the study explores the hydrodynamic performance of the toroidal propeller, including pressure distribution on the blade surface and the flow field dynamics in the propeller's wake across varying advance coefficients. Subsequently, it examines the influence of geometric parameters such as axis span ratios, lateral angles, roll angles, and vertical angles, on the hydrodynamic performance, elucidating their effects on the pressure distribution profiles at radial sections, thrust coefficient, and open water efficiency. The findings provide substantial guidance for the geometric parameter selection in the optimal design of future toroidal propellers.

Effect of pore plugging on transpiration cooling performance implemented with perforated flat plates

Transpiration cooling is expected to be one of the most effective cooling technologies for gas turbine blades. However, dust deposition can easily lead to pore plugging, which limits its practical application in engineering. The plugging of pores in conventional porous media models is difficult to predict, so it is essential to construct computational models with finely controllable porous structures. In this study, a transpiration cooling configuration with straight holes perforated with a diameter of 0.5 mm was adopted. The effects of plugging ratio and region on flow and heat transfer under different coolant injection ratios and pump power conditions were investigated by numerical simulation. The results showed that at constant mass flow rate, the overall cooling performance increased as the plugging ratio increased at low injection ratios. At high injection ratios, the opposite was true. With a certain pump power, as the plugging rate increased, the flow loss coefficient rose, leading to a decrease in cooling efficiency. Transpiration cooling efficiency was also highly sensitive to the distribution of plugged holes at the same plugging ratio. When the coolant flow was sufficient, the farther the plugged area was from the leading edge, the smaller the reduction in cooling efficiency.

Evaluation of a Multimodal Confocal Therapeutic Focused Ultrasound Apparatus: Bridging Cavitation, Thermal Ablation, and Histotripsy in Preclinical Treatments

ObjectivesThe development of versatile and user-friendly preclinical platforms is vital for therapeutic ultrasound research. We introduce a flexible ultrasound-guided focused ultrasound (FUS) platform with two confocal therapeutic transducers, allowing thermal and mechanical modalities, and present its design and, features, with validation of potential applications in preclinical studies. MethodsThe probe's acoustic properties, energy delivery efficiency, and thermal and mechanical modalities are characterized. A computational model predicts thermal effects while optimizing treatment parameters. Ex vivo tissue samples are used to validate system performance, safety, and usability. In vivo experiments on mice with MC38 tumors are presented with immunohistochemistry (IHC) to validate treatment outcomes. ResultsElectroacoustic conversion efficiency levels were 80% and 40% for 1.1 MHz and 3.3 MHz, respectively. Confocal therapy transducers at 1.1 MHz and 3.3 MHz successfully demonstrated cavitation histotripsy and thermal treatments. At 1.1 MHz, for histotripsy, −20 MPa negative peak pressure is achieved, while at 3.3 MHz used for thermal ablation a maximum of 35 MPa is reached for positive peak pressure. Numerical analysis provides thermal treatment planning, aligning with in vitro and in vivo experiments for lesion prediction. Real-time in vivo cavitation monitoring was consistent with in vitro chemical dosimetry, ensuring treatment uniformity. ConclusionThe ultrasound platform induces thermal or mechanical lesions with precise spatial resolution, validated by IHC tissue characterization. Integrated cavitation monitoring enables real-time treatment monitoring. Coupling with thermal simulations provides optimization of thermal treatment parameters. This versatile “all-in-one” therapeutic platform supports multiple treatment modalities including cavitation, thermal ablation, and histotripsy, facilitating direct comparisons to assess their efficacy in diverse therapeutic settings.

Artificial intelligence-based weather prediction framework using neural networks

For humans, weather prediction is vital in making rational everyday choices and avoiding risk. Accurate weather forecasting is regarded as one of the world’s most difficult issues. New weather forecasting, unlike conventional techniques, relies on a mixture of computer simulations, observation (via balloons and satellites), and information of patterns and trends (via local weather analysts and weather stations). Predictions are rendered with fair precision using such techniques. Prediction algorithms based on complicated formulas run the majority of computational models used for prediction. This paper highlights the prediction of weather with the artificial neural networks (ANN) using the latest available smart computing devices. To assess the effectiveness of the model, comparison research is conducted with the other existing models in the same area. The result demonstrates that our approach is better in comparison to other similar research and products. The comparative analysis has been undergone which confirms the superiority of our proposed techniques with an accuracy of 90.4%.

Unveiling the Anti-convulsant Potential of Novel Series of 1,2,4-Triazine- 6H-Indolo[2,3-b]quinoline Derivatives: In Silico Molecular Docking, ADMET, DFT, and Molecular Dynamics Exploration.

Epilepsy is a chronic neurological disorder caused by irregular electrical activity in the brain. To manage this disorder effectively, it is imperative to identify potential pharmacological targets and to understand the pathophysiology of epilepsy in depth. This research aimed to identify promising leads from a library of 1,2,4-triazine-6Hindolo[ 2,3-b]quinoline derivatives and optimize them using in silico and dynamic processes. We used computational studies to examine 1,2,4-Triazine-6H-indolo[2,3-b]quinoline derivatives. Some methods were used to strengthen the stability of binding sites, including Docking, ADMET, IFD, MMGBSA, Density Functional Theory (DFT), and Molecular Dynamics. HRSN24 and HRSN34 exhibited promising pharmacokinetic and pharmacodynamic characteristics compared to standard drugs (Carbamazepine and Phenytoin) and a co-crystal ligand (Diazepam). Both HRSN24 and HRSN34 presented notable Glide Xp docking scores (-4.528 and -4.633 Kcal/mol), IFD scores (-702.22 and -700.3 Kcal/mol), and MMGBSA scores (-45.71 and -14.46 Kcal/mol). HRSN24 was selected for molecular dynamics and DFT analysis. During MD, HRSN24 identified LYS21, GLY22, ASP24, ARG26, VAL53, MET55, and SER308 as the most important amino acid residues for hydrophobic interactions. A DFT computation was performed to determine the physicochemical properties of HRSN24, revealing a total energy of -1362.28 atomic units, a HOMO value of -0.20186, and a LUMO value of -0.01915. Based on computational modelling techniques, an array of 1,2,4-triazine-6H-indolo [2,3-b]quinoline derivatives were evaluated for their anti-convulsant properties. A stable compound within the GABAA receptor was identified by HRSN24, suggesting its affinity as an anti-convulsant.

An analysis of diverse computational models for predicting student achievement on e-learning platforms using machine learning

An analysis of diverse computational models for predicting student achievement on e-learning platforms using machine learning

Electrical circuit approaches to modeling brain chaos: insights into neural dynamics

This paper investigates the simulation of brain chaos dynamics using a combination of the Chua circuit and diode tunnel mechanisms, aiming to examine chaotic behavior in brain networks. Leveraging the inherent chaotic properties of the Chua circuit, Fitzhugh-Nagumo (FHN) function and the nonlinear characteristics of diode tunneling, our model offers a platform to mimic the intricate synaptic interactions observed in the brain. By subjecting the model to various stimuli and perturbations, we analyze the emergence and evolution of chaotic patterns, shedding light on the underlying mechanisms of cerebral chaos. Through numerical simulations and experimental validation, we demonstrate the effectiveness of our approach in replicating key features of brain chaos and highlight its potential implications for understanding neurological disorders and cognitive processes. This research contributes to the broader effort of leveraging computational models to explore the complex dynamics of the brain and their implications for neuroscience and microengineering.